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Research paper
Exploratory analysis of neuropsychological and neuroanatomical correlates of progressive mild cognitive impairment in Parkinson's disease
  1. Ji E Lee1,
  2. Kyoo H Cho1,
  3. Sook K Song2,
  4. Hee Jin Kim1,
  5. Hye Sun Lee3,
  6. Young H Sohn1,
  7. Phil Hyu Lee1,4
  1. 1Department of Neurology and Brain Research Institute, Yonsei University College of Medicine, Seoul, Korea
  2. 2Department of Neurology, Jeju University College of Medicine, Jeju, Korea
  3. 3Department of Biostatistics or Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea
  4. 4Severance Biomedical Science Institute, Seoul, Korea
  1. Correspondence to Professor P H Lee, Department of Neurology, Yonsei University Medical College, 250 Seongsanno, Seodaemun-gu, Seoul 120–752, South Korea; phisland{at}chol.net

Abstract

Background Parkinson's disease with mild cognitive impairment (PD-MCI) is a heterogeneous entity in terms of cognitive profiles and conversion to dementia. However, the risk factors for ongoing cognitive decline in patients with PD-MCI are not clearly defined.

Methods 51 patients with PD-MCI were prospectively followed-up for a minimum of 2 years. Subjects were classified as MCI converters (n=15) or MCI non-converters (n=36) based on whether they were subsequently diagnosed with PD dementia. We explored cognitive profiles and neuroanatomical characteristics of PD-MCI converters using voxel based morphometry (VBM) of grey matter (GM) density and region of interest based volumetric analysis of the substantia innominata (SI).

Results PD-MCI converters showed more severe cognitive deficits in frontal executive functions, immediate verbal memory and visual recognition memory compared with PD-MCI non-converters. VBM analysis revealed that PD-MCI converters had significantly lower GM density in the left prefrontal areas, left insular cortex and bilateral caudate nucleus compared with that in PD-MCI non-converters. The mean normalised SI volume was significantly smaller in both PD-MCI converters (1.19±0.35, p<0.001) and PD-MCI non-converters (1.52±0.27, p<0.001) compared with that in controls (1.87±0.19). PD-MCI converters had a significantly smaller normalised SI volume than PD-MCI non-converters (p<0.001).

Conclusions Our data show that atrophy in the frontostriatal areas and cholinergic structures, as well as frontal lobe associated cognitive performance, may act as predictors of dementia in PD-MCI patients, suggesting distinctive patterns of cognitive profiles and a neuroanatomical basis for progressive PD-MCI.

  • Parkinson'S Disease
  • Neuroanatomy
  • Dementia
  • Cognition
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Introduction

Mild cognitive impairment (MCI) is a transitional state between normal aging and dementia that has been used for early detection and treatment of dementia.1 Several studies have demonstrated that a substantial portion of patients with Parkinson's disease (PD) have quantifiable cognitive deficits that do not meet the criteria for dementia, and these patients are categorised as PD-MCI.2 Recent epidemiological studies have shown that one-fifth of PD patients are classified as having MCI, and about 19% of patients with untreated early PD have MCI.3

In common with the MCI of Alzheimer's disease (AD) prototype, PD-MCI is a heterogeneous entity in terms of cognitive profiles and conversion to dementia. Ample evidence has suggested that memory impairment, medial temporal atrophy, APO E and β amyloid load in patients with MCI are important determining factors for conversion to AD.4 ,5 Meanwhile, risk factors for ongoing cognitive decline in patients with PD are not clearly defined. Neuropsychological predictions of dementia in PD (PDD) in longitudinal studies are conflicting; cognitive performance associated with posterior cortical areas, such as semantic fluency and visuoconstructional ability, has been suggested to be an important determinant for ongoing cognitive decline of PD6 ,7 whereas frontal executive functions are also considered to be significant predictors of PDD.8–10 Analyses of neuroimaging predictors of PDD are rare, and one functional neuroimaging study reported that the status of cerebral glucose metabolism in the posterior visual association cortical areas and posterior cingulate areas was a significant predictor of dementia in patients with PD.11

A neural basis for cognitive dysfunctions in PD remains unknown, even though several neurochemical, neuroanatomical and pathological candidates are suggested for PD related cognitive decline.12 Of those, the cholinergic system arising from the nucleus basalis of Meynert located in the substantia innominata (SI) of the basal forebrain has been suggested to be one of most important neural systems responsible for cognitive dysfunction in PD patients.13 ,14

In the present study, we explored the cognitive profiles and neuroanatomical characteristics of PDD converters from patients with PD-MCI using voxel based morphometry (VBM) and region of interest based volumetric analysis of the SI to further elucidate the neuropsychological and neuroanatomical predictors of ongoing dementia in patients with PD.

Patients and methods

Subjects

This prospective cohort study enrolled 51 patients with PD-MCI from a university hospital. Subjects completed an MRI protocol and underwent neuropsychological tests for assessment of cognitive dysfunction at baseline. All study subjects completed the MR protocol and neuropsychological testing within a 3 week interval. Change in cognition in patients was evaluated annually using a practical set for diagnosis of PDD (level I) recommended by the Movement Disorder Society Task Force,15 and a minimal follow-up period of 2 years was required for inclusion in the study. If the patient had evidence of PDD on the first practical set of level I tests in a follow-up period, we introduced a detailed neuropsychological test. PD was diagnosed according to the clinical diagnostic criteria of the UK PD Society Brain Bank.16 Motor symptoms were assessed using the Unified PD Rating Scale part III (UPDRS- III). Total medication dose for PD was calculated in levodopa equivalents.17 Based on predominant motor symptom at baseline, patients were divided into one of three subtypes: predominance of tremor, predominance of hypokinetic–rigidity, and predominance of postural instability–gait disorder.18 Subjects who had equal dominance of symptoms in two or three categories were classified as mixed. We used the Seoul Neuropsychological Screening Battery to determine cognitive subsets in the diagnosis of PD-MCI and PDD.19 ,20 The Seoul Neuropsychological Screening Battery includes the cognitive subsets of attention (forward and backward digit span and letter cancellation tests), language and related functions (reading, writing, comprehension, repetition, confrontational naming using the Korean version of the Boston Naming Test), visuospatial function (drawing an interlocking pentagon and the Rey Complex Figure Test (RCFT)), verbal memory (the Seoul Verbal Learning Test), visual memory (RCFT; immediate recall, 20 min delayed recall and recognition) and frontal executive function (contrasting programme, go-no-go test, Luria loop, phonemic and semantic Controlled Oral Word Association Test, and Stroop test). The pentagon scoring system used in this study was a 6 point hierarchical scale, where 6 represented a perfect attempt and 1 the worst attempt.21 Age, sex and education specific norms for each test based on 447 normal subjects are available.22 The scores of these quantifiable cognitive tests were classified as abnormal when they were below the 16th percentiles of the norms for age, sex and education matched normal subjects.

We considered attention function to be abnormal if at least two of the three items were abnormal. The frontal/executive function tests were classified into three groups: motor executive function, Controlled Oral Word Association Test and the Stroop test. Frontal/executive function was considered to be abnormal when at least two of three tests were abnormal. A diagnosis of MCI was made if the patient met the following criteria: (1) a subjective cognitive complaint, (2) at least one of the cognitive domains was abnormal,20 ,23 (3) scores for the Korean version of the Mini-Mental State Examination (K-MMSE) were above the 16th percentile for the age and education appropriate norm and (4) no evidence of abnormal activities of daily living, judged clinically and by a Korean instrumental activities of daily living scale.22 The self-rated Beck Depression Inventory was used to assess depressive symptoms in patients with PD.24 During a mean follow-up period of 2.6 years (range 2.0–3.6 year), subjects with PD-MCI were reclassified as MCI converters (n=15) or MCI non-converters (n=36) based on whether they were subsequently diagnosed with dementia. There was no significant difference in the follow-up period between PD-MCI converters (2.7 years) and PD-MCI non-converters (2.5 years). PDD was diagnosed according to the clinical diagnostic criteria for probable PDD.25

Exclusion criteria included evidence of focal brain lesions on MRI or the presence of other neurodegenerative diseases that might account for dementia. Possible medical comorbidities were excluded by laboratory tests, including the thyroid function test, vitamin B12 and folic acid levels, and the Venereal Disease Research Laboratory test. A [18F] FP-CIT positron emission tomography scan was performed on 36 of the patients with PD-MCI (11 PD-MCI converters and 25 PD-MCI non-converters), and all showed decreased dopamine transporter uptake in the posterior putamen. Healthy age and sex matched volunteers were used as controls for imaging analysis (n=25, age=70.0±3.4 years). They were recruited by advertisements about the project or were healthy relatives of patients with movement disorders or dementia. Control subjects had no active neurological disorders, no cognitive complaints and a minimum score of 28 on the K-MMSE. This study was approved by the Yonsei University Severance Hospital ethical standards committee on human experimentation for experiments using human subjects. Written informed consent was obtained from all subjects participating in this study.

MRI acquisition

All scans were acquired using a Philips 3.0 T scanner (Philips Intera; Philips Medical System, Best, The Netherlands) with a SENSE head coil (SENSE factor=2). A high resolution T1 weighted MRI volume dataset was obtained from all subjects using a three-dimensional T1-TFE sequence configured with the following acquisition parameters: axial acquisition with a 224×256 matrix; 256×256 reconstructed matrix with 182 slices; 220 mm field of view; 0.98×0.98×1.2 mm3 voxels; TE 4.6 ms; TR 9.6 ms; flip angle 8°; and slice gap 0 mm.

VBM of grey matter

VBM was conducted using DARTEL26 in the SPM8 software (Institute of Neurology, University College London, UK). A group of grey matter (GM) templates were generated from control groups to which all individual GM was spatially normalised. Spatially normalised GM maps were modulated by the Jacobian determinant of the deformation field to adjust volume changes during non-linear transformation. GM maps were smoothed using a 6 mm full width at half maximum isotropic Gaussian kernel. Regional volume differences were determined using one way analysis of variance at every voxel in the GM from PD patients and controls, where age, sex and GM volume were included as covariates in the analysis of covariance. Age, sex, duration of PD, total GM volume and K-MMSE score were also included as covariates in the analysis of covariance when analysing GM density between PD-MCI converters and PD-MCI non-converters. Statistical significance was determined at uncorrected p<0.001 with cluster size >50 voxels. Additionally, we searched for significant brain areas in which GM density correlated with frontal executive function tasks (phonemic fluency, semantic fluency, word score in Stroop test and colour–word score in the Stroop test) using multiple regression models covariated with age, sex, duration of PD and total GM volume.

Volumetric determination of SI

Volumes of the SI were determined by manually delineating the boundaries of this structure with MRIcro software 27 on the coronal T1 weighted MRI scans. Delineation of the SI on MRI was based on the method reported previously by George et al.28 Briefly, with the first section at the level of the crossing of the anterior commissure, the ventral aspect of the globus pallidus demarcated the dorsal border of the SI whereas the ventral border was the base of the brain containing the anterior perforated space. The medial border of the SI was operationally defined by a vertical line extending from the ventrolateral border of the stria terminalis to the base of the brain. The lateral border extended to the medial aspect of the putamen. In the second section traced, the anterior commissure might be uncrossed. The third section evaluated was at the level of the emergence of the anterior commissure from the temporal lobe. The anatomical landmarks used to define the borders of the SI were applied to all three consecutive sections. Total SI volume calculated included both the right and left hemispheres. To correct for individual brain size, volumes were normalised by dividing by total intracranial volume derived from a sagittal section with a 5 mm thickness. Intracranial volume was calculated by tracing the inner table of the cranium in consecutive sagittal sections spanning the entire brain. At the level of the foramen magnum, a straight line was drawn from the inner surface of the clivus to the most anterior extension of the occipital bone. Normalised SI volume was defined by the following formula: total SI volume (mm3)/total intracranial volume (mm3)×10 000. Tracings were performed blindly (by JEL and HJK), and intra-rater and inter-rater reliability, expressed as correlation coefficients, were 0.86 and 0.83, respectively.

Statistical analysis

The Kruskal–Wallis test or one way analysis of variance followed by post hoc comparisons was used to assess group differences in categorical and continuous variables, respectively. To determine whether the two groups differed on cognitive performance, we compared neuropsychological measures using a multivariate analysis of variance. A logistic regression analysis in a forward method was used to estimate the neuropsychological predictors of PD-MCI converters. Independent variables were delayed visual memory, semantic and phonemic fluency, the contrasting programme and colour–word score, which showed a significant difference in cognitive performance between the groups. Statistical analyses were performed using commercially available software (SPSS, V.18.0), and a two tailed p value <0.05 was considered significant.

Results

Demographic characteristics and neuropsychological analysis of PD-MCI converters and PD-MCI non-converters

The baseline demographic characteristics of the subjects are shown in table 1. No significant differences were observed between PD-MCI converters and PD-MCI non-converters for age, gender, educational level, general cognitive deficits (measured by the K-MMSE), the Clinical Dementia Rating scale, Beck Depression Inventory scores, duration of parkinsonism, duration of cognitive impairment, Hoehn and Yahr stage, UPDRS motor scores, motor phenotype, levodopa equivalent dosage or total intracranial volume. No patient presented with predominant postural instability–gait disorder symptoms. Detailed neuropsychological test results are shown in table 2. PD-MCI converters showed more severe baseline cognitive deficits for immediate verbal memory (12.7 vs 16.7, p=0.006), visual recognition memory (17.3 vs 18.8, p=0.005), contrasting programme (17.8 vs 19.7, p<0.001), phonemic fluency (12.5 vs 21.2, p=0.005), semantic fluency (19.4 vs 28.1, p<0.001), the word Stroop test (96.4 vs 110.4, p=0.001) and the colour–word test (34.8 vs 72.0, p<0.001) compared with PD-MCI non-converters. A logistic regression analysis after adjusting for age, gender, education and PD duration revealed that performance on the colour–word test (OR 1.06; 95% CI 1.01 to 1.10), the word Stroop test (OR 1.12; 95% CI 1.02 to 1.23) and semantic fluency (OR 1.18; 95% CI 1.00 to 1.38) were significant independent predictors of PD-MCI converters.

Table 1

Demographic characteristics between PD-MCI converters, non-converters and controls

Table 2

Neuropsychological data in PD-MCI converters and non-converters

GM analysis of PD-MCI converters and PD-MCI non-converters

PD-MCI non-converters had significantly lower GM density in the right middle frontal cortex and bilateral parietal areas with a left-sided predominance compared with that in controls (figure 1). In PD-MCI converters, however, the area of lower GM density relative to controls was located in the insular cortex, extending into the inferior frontal area, bilateral parahippocampal gyrus, left cerebellum and left caudate nucleus (figure 1). The anatomical locations of the areas are listed in table 3. In a comparison between PD-MCI converters and PD-MCI non-converters, PD-MCI converters had significantly lower GM density in the left frontal areas, left insular cortex and bilateral caudate nucleus compared with that in PD-MCI non-converters (figure 2). We found no area where PD-MCI non-converters had more GM atrophy than PD-MCI converters. The anatomical locations of the areas are listed in table 4.

Table 3

Anatomical location of areas showing significant difference in grey matter density between PD-MCI converters or non-converters, and controls

Table 4

Anatomical location of areas showing significant differences in grey matter density between PD-MCI converters and non-converters

Figure 1

Voxel based morphometry analysis in Parkinson's disease with mild cognitive impairment (PD-MCI) converters and non-converters, compared with healthy subjects. PD-MCI non-converters had significantly lower grey matter (GM) density in the bilateral parietal and right middle frontal areas compared with that in controls (left panel). In PD-MCI converters, the areas of lower GM density relative to controls were located in the bilateral parahippocampal gyrus, insular cortex, left inferior frontal gyrus, left caudate nucleus and left cerebellum (right panel). Access the article online to view this figure in colour.

Figure 2

Direct comparison of grey matter (GM) densities between Parkinson's disease with mild cognitive impairment (PD-MCI) converters and non-converters. PD-MCI converters had more GM atrophy in the left prefrontal areas, left insular cortex and bilateral caudate nucleus compared with PD-MCI non-converters. The colour bar represents T values. Access the article online to view this figure in colour.

Correlation of GM densities and frontal executive function tasks in PD-MCI converters

A decline in phonemic fluency was positively correlated with GM atrophy in the left superior frontal gyrus (figure 3A), and semantic fluency decline was positively correlated with GM atrophy in the right middle frontal gyrus (figure 3B). Cognitive performance on the word Stroop test was positively correlated with GM density in the right superior frontal gyrus and in the left middle frontal gyrus (figure 3C), and performance on the colour–word test was positively correlated with GM density in the left middle frontal gyrus (figure 3D). The anatomical locations of the areas are listed in the online supplementary table S1.

Figure 3

Correlation between frontal executive function tasks and grey matter (GM) densities in Parkinson's disease with mild cognitive impairment (PD-MCI) converters. A decline in phonemic fluency was positively correlated with GM atrophy in the left superior frontal gyrus (A), and a decline in semantic fluency was positively correlated with GM atrophy in the right middle frontal gyrus (B). A decline in word score in the Stroop test was positively correlated with GM atrophy in the right superior frontal gyrus and in the left middle frontal gyrus (C), and a decline in colour–word score in the Stroop test was positively correlated with GM atrophy in the left middle frontal gyrus (D). Access the article online to view this figure in colour.

Comparison of SI volume between PD-MCI converters and PD-MCI non-converters

Brain MRI of the area containing the SI and mean normalised SI volumes among the groups are shown in figure 4. No significant difference in total intracranial volume was found among the groups (table 1). Mean normalised SI volume was significantly smaller in both PD-MCI converters (1.19±0.35, p<0.001) and PD-MCI non-converters (1.52±0.27, p<0.001) compared with that in controls (1.87±0.19). In a comparison between PD-MCI converters and PD-MCI non-converters, PD-MCI converters had a significantly smaller normalised SI volume than PD-MCI non-converters (p<0.001) (figure 4B).

Figure 4

Brain MRI illustrating the area containing the substantia innominata (SI, red outline) in controls (A-a), in Parkinson's disease with mild cognitive impairment (PD-MCI) converters (A-b) and in PD-MCI non-converters (A-c), and a comparative analysis of normalised SI volume among the groups (B). The first section of the brain MRI indicates the region of interest at the level of the crossing of the anterior commissure (red arrow), the second section of the brain MRI is at the level at which the anterior commissure might be uncrossed and the third section of the brain MRI is at the level at which the anterior commissure emerges from the temporal lobe. Mean normalised SI volume was significantly smaller in PD-MCI converters compared with PD-MCI non-converters (B). Bars indicate SE. *p<0.001. Access the article online to view this figure in colour.

Discussion

The present study demonstrated, for the first time, that compared with PD-MCI non-converters, PD-MCI converters had lower GM density in the prefrontal area and in the caudate nucleus, with a smaller SI volume. Additionally, PD-MCI converters had lower cognitive performance scores in frontal executive and visual memory functions compared with PD-MCI non-converters. These data suggest that a distinctive neuropsychological profile and neuroanatomical bases may help predict conversion to dementia in patients with PD-MCI.

Growing evidence from pathological and in vivo functional neuroimaging studies has demonstrated that degeneration of the basal forebrain cholinergic system appears early in patients with PD, prior to the occurrence of dementia, and deteriorates further during dementia.14 ,29 ,30 In our previous MR based volumetric analysis, we showed that loss of SI volume begins in the early stages of PD and appears to be profound as cognitive function worsens, becoming most severe in PDD.31 In the present study, we demonstrated for the first time that PD-MCI converters had a significantly smaller SI volume compared with PD-MCI non-converters, where SI volume of PD-MCI converters was comparable with PDD status in our previous study. Generally, cholinergic inputs from the basal forebrain play a key role in attention, performance on frontal lobe dependent tests and memory function through their connections with frontal or basolateral limbic areas.32 In patients with PD, reduced cholinergic activity in the cortex secondary to degeneration of the nucleus basalis of Meynert constitutes one of the main mechanisms underpinning cognitive dysfunction, and is significantly correlated with cognitive performance. In this regard, our results indicate that baseline cholinergic activity from the basal forebrain seems to be an important predictor of ongoing cognitive decline in patients with PD, inferring the use of cholinesterase inhibitors in this group. A further study using functional imaging with a cholinergic ligand is required to clarify the role of the cholinergic system as an important prognostic factor for ongoing cognitive decline.

In a recent longitudinal study of a population based PD patient cohort, Williams-Gray and colleagues6 ,33 demonstrated that more posterior cortically based cognitive deficits, such as temporal and parietal lobe functions, evolved into later ongoing dementia, whereas frontostriatal executive deficits were not associated with subsequent dementia risk. Specifically, they argued that semantic fluency and ability to draw an interlocking pentagon were the most significant predictors of cognitive decline in patients with PD. Meanwhile, other studies have reported that along with verbal fluency, frontal executive functions were also considered to be significant predictors of PDD.8–10 An analysis of cognitive profiles in the present study confirmed semantic and phonemic verbal fluency as recognition memory and frontal lobe based cognitive performance, such as contrasting programme, and the word and colour–word score were significantly associated with PD-MCI conversion. In particular, among cognitive factors, frontal executive function was an independent predictor of PD-MCI conversion. In contrast with previous data, visuoconstructional performance assessed by RCFT or the pentagon copy test was not related to PD-MCI conversion in our study. Thus our data imply that neuropsychological performance associated with the frontal lobe appears to be a predictor of conversion to dementia in patients with PC-MCI whereas parietal lobe based cognitive performance was not. Possible explanations for the role of frontal lobe based cognition as a predictor of PDD remain speculative as there is no information on longitudinal pathological or imaging data. Mattila et al34 ,35 reported in cross sectional pathological studies that Lewy body burden and reduced choline acetyltransferase activity in the frontal area were significantly associated with cognitive decline in patients with PD, and Lewy body burden and choline acetyltransferase activity exhibited a negative relationship. Bohnen et al36 ,37 demonstrated in functional imaging studies that cholinergic denervation in non-demented PD is strongly associated with impairment in frontal executive tasks and memory. Accordingly, increased Lewy body burden in the frontal area and derangement in the cholinergic system may underlie neuropathological correlates of frontal lobe based cognitive performance as a predictor of ongoing dementia in PD. In addition, with the heterogeneity of the neurocognitive profile in PD patients, differences in study designs, which may lead to discrepant results between the present study and the CamPalgn study, deserve a mention. Differences in sample size, diagnostic criteria used to define dementia and follow-up period from baseline, as well as neuropsychological tests used to evaluate cognitive subsets of memory and visuospatial functions, may lead to discrepant results between the present study and the CamPalgn study. More importantly, baseline cognition differed between PD patients in the present study and that in the CamPalgn study; the present study enrolled patients with PD-MCI whereas the CamPalgn study included patients with PD-MCI and cognitively normal PD. Because patients with PD-MCI have a greater risk of dementia than cognitively normal PD patients,38 this difference in baseline cognitive status may also have impacted on the neuropsychological results.

VBM analysis showed that reduced GM density in PD-MCI converters relative to that in PD-MCI non-converters was localised in the prefrontal areas, insular cortex and caudate nucleus. Cortical atrophy in the prefrontal areas corresponds well to neuropsychological characteristics of PD-MCI converters showing poorer performance in frontal executive tasks compared with PD-MCI non-converters, and cognitive performance on frontal executive function tasks was positively correlated with prefrontal GM densities. This result further supports the fact that frontal atrophy and its related cognitive dysfunctions are important determinants of future cognitive decline in patients with PD. The insula is known to be a major relay and integrative site for interoceptive information and appears to represent an important link between autonomic, affective and cognitive processes.39 ,40 In particular, the insular cortex could impact on cognitive processes of the frontal lobe through interconnections with the prefrontal cortex and anterior cingulated,39–41 and thus the insular atrophy may impact additionally on frontal lobe based cognitive performance. Caudate nucleus atrophy in PD-MCI converters relative to PD-MCI non-converters was an unexpected and interesting finding. Indeed, along with cortical pathology, the striatal pathology of either α-synuclein or β-amyloid has been suggested to have an important role in cognitive dysfunction in PD. Tsuboi et al42 reported that α-synuclein pathology in the striatum was common and greater in patients with PDD compared with non-demented patients with PD. Additionally, Kalaitzakis et al43 and Jellinger44 demonstrated that the β-amyloid burden in the striatum was significantly greater in PDD cases compared with that in non-demented PD cases. Recent studies have provided evidence that β-amyloid and α-synuclein may interact in a synergistic manner, such that β-amyloid promotes α-synuclein aggregation and neuronal toxicity by stabilising the formation of hybrid nanopores.45 This interaction is supported by pathological and in vivo PiB imaging evidence that the β-amyloid burden, in addition to α-synuclein per se, accelerates dementia in patients with PDD or dementia with Lewy bodies.46 ,47 In patients with PD-MCI, evidence from pathological and biomarker studies has suggested that Lewy bodies and AD-like pathology may also be major contributors to PD-MCI.48–50 Furthermore, a biomarker study demonstrated that reduced CSF amyloid β1–42 was an independent predictor of future cognitive decline in patients with non-demented PD, suggesting an important role of AD-like pathology in the development of dementia in PD.51 Therefore, a greater burden of AD or PD pathology in the striatum and its interaction may lead to more striatal atrophy in PD-MCI converters relative to PD-MCI non-converters, and this might act as a neuroanatomical predictor for ongoing cognitive decline in patients with PD-MCI. However, a longitudinal study with PiB imaging is warranted to validate the role of β-amyloid burden as a contributor to cognitive decline in PD-MCI as a recent PiB positron emission tomography study reported that β–amyloid burden in patients with non-demented PD did not differ from that in controls.52

The strengths and limitations of the present study need to be addressed. With the introduction of a detailed full neuropsychological battery to evaluate cognitive performance, dopamine transporter imaging was used to determine underlying PD pathology in about 70% of the study subjects, which would exclude cases of parkinsonism associated with AD pathology as possible. Nevertheless, as this study did not include any autopsy proven data, we could not exclude the influence of AD-like pathology in our patients with PD-MCI because some proportion of MCI exhibits parkinsonism.53 Second, because of the limited sample size of PD-MCI converters and the relatively short follow-up period, our results should be interpreted cautiously until a future large scale study delineates the neural and neurochemical bases determining ongoing dementia in patients with PD. Third, the diagnosis of MCI in the present study was not based on the diagnostic criteria of the Movement Disorder Society Task Force guidelines,54 and the criteria used in the present study did not meet the level 2 category. Therefore, it is possible that false positive patients with PD-MCI may have been included in this study. Finally, because an uncorrected threshold used in the present study may not fully protect against results due to chance, the results may be prone to false positives. Therefore, the significant clusters found in the present study need to be validated further.

In summary, our data demonstrated that atrophy in the frontal area, caudate and cholinergic structures, as well as frontal lobe associated cognitive performance, may act as predictors of dementia in patients with PD. The results of present study imply that neuropsychological profiles and neuroanatomical bases may be distinctive between PD-MCI converters and PD-MCI non-converters.

References

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Footnotes

  • Contributors The study was conceived and planned by JEL and PHL. JEL, KHC, SKS, and HJK collected and analysed the data, and was involved in the initial drafting of the manuscript. YHS collected the data and revised the paper. HSL implemented the statistical analyses. PHL was involved in the final approval of the version to be published, and takes responsibility for the integrity and accuracy of the data analyses.

  • Funding This study was supported by a grant from the Korea Healthcare technology R&D project, Ministry for Health, Welfare and Family Affairs, Republic of Korea (A091159).

  • Competing interests None.

  • Ethics approval The study was approved by Yonsei University Severance Hospital ethical standards committee.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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