Cognitive and Neuroanatomical Correlates in Early Versus Late Onset Parkinson’s Disease Dementia

Abstract

Background: Aging is the most important risk factor of development of dementia in Parkinson’s disease (PD), but there are no data on clinical and radiological heterogeneity of PD dementia (PDD) depending on age at onset.

Objectives: The goal of this study was to examine whether patients with PDD are clinically and radiologically heterogeneous depending on age at onset.

Methods: A total of 116 patients with PD dementia and 121 age- and sex-matched normal controls were enrolled. The subjects were divided into early-onset (EOPDD; n = 39) and late-onset (LOPDD; n = 77) PDD with the respective age-matched control group based on a cutoff value of 70 years. The effects of diagnosis, age, and their interaction on neuropsychological tests, cortical thickness, and substantia innominata volume were assessed using analysis of covariance.

Results: EOPDD patients had a poorer cognitive performance on digit backward, forward span test (p = 0.011 and 0.05), and visual recognition memory function (p = 0.012) compared with LOPDD patients. Additionally, EOPDD patients exhibited cortical thinning in the left anterior cingulate gyrus and the right inferior temporal gyrus, with significantly decreased normalized substantia innominata volume (p = 0.044).

Conclusions: Our data demonstrated that EOPDD patients exhibit poorer cognitive performance and more severe atrophy in the cortex and substantia innominata, implying that EOPDD may be a distinct phenotype different from LOPDD.

Keywords

Age at onset neuroanatomical correlates neurocognitive performance Parkinson’s disease dementia

INTRODUCTION

Cognitive dysfunction in Parkinson’s disease (PD) is one of the most disabling non-motor features, leading to poorer performances of daily living and increase in caregiver burden. Even though the risk factors of ongoing cognitive decline in PD are not clearly defined, several clinical and radiological predictors have been suggested. A longitudinal study suggested that cognitive performance associated with posterior cortical areas, such as semantic fluency and visuoconstructional ability, appears an important determinant for development of PD dementia (PDD) [1], although frontal executive functions are also considered a significant predictor of PDD [2]. Additionally, poor performance in attention at baseline is an independent predictor of cognitive decline in PD [3]. In aspects of functional neuroimaging, the status of cerebral glucose metabolism in posterior visual association and posterior cingulate areas was a significant predictor of dementia in patients with PD [4]. Moreover, the nucleus basalis of Meynert located in the substantia innominata (SI) that is the major source of cholinergic input to the cerebral cortex [5] and may represent cholinergic activity in the cerebral cortex was suggested as an important neural system responsible for cognitive dysfunctions and ongoing cognitive decline in PD patients [6, 7].

Ample evidence has showed that aging is the most important risk factor of development of PDD. Prospective studies showed a relationship between age and cognitive decline, with particular susceptibility in those older than 70 years [1], and that aging might accelerate cognitive decline after the age of 70 years [8]. Despite lower incidence [9], a small number of PD patients experience dementia in early stage of disease course as other neurodegenerative diseases. However, the early-onset form of PDD has not yet to be focused clinically. In the present study, we analyzed neuropsychological profiles and radiological patterns of cortical thinning and SI volume in patients with early-onset and late-onset PDD to examine whether patients with PDD represent clinical and radiological heterogeneity depending on age at onset.

MATERIALS AND METHODS

Subjects

The present study enrolled 116 patients with PDD recruited from the Movement Disorders and Dementia outpatient clinic at Yonsei University Severance Hospital from March 2007 to December 2014. PD was diagnosed according to the clinical diagnostic criteria of the UK PD Society Brain Bank [10]. PDD was diagnosed based on the Movement Disorder Society consensus criteria for diagnosis of probable PDD [11]. We enrolled only patients showing decreased dopamine transporter uptake in the posterior putamen on ¹⁸F-FP-CIT PET scans. Patients who experienced cognitive impairment that was sufficient to meet criteria for dementia within 1 year after onset of PD were not included to exclude patients who met research criteria for dementia with Lewy bodies.

Depending on the age at PDD diagnosis, the patients were divided into early-onset PDD (EOPDD <70 years of age, n = 39) and late-onset PDD (LOPDD ≥ 70 years of age, n = 77). Motor symptoms were assessed using the Unified PD Rating Scale part III (UPDRS- III) in the ‘off’ status. Disease duration and memory complaints in each patient were based on interviews with patients and caregivers living with patients. History of vascular risk factors was defined based on the use of agents. All patients participated in the Seoul Neuropsychological Screening Battery (SNSB) [12], consisting of the following cognitive subsets: attention, language and related functions (Korean version of the Boston Naming Test and calculation), visuospatial function (Rey Complex Figure Test; RCFT), verbal memory (three-word registration and recall and the Seoul Verbal Learning Test), visual memory (RCFT, immediate recall, 20-minute delayed recall, and recognition), and frontal/executive function (contrasting program, go-no-go test, phonemic and semantic fluency test, and Stroop test). Among study subjects, MRI scans used for cortical thickness and SI volume analysis with protocols discussed below were available in 65 patients with PDD. Of these 65 patients, 25 were classified as EOPDD and 40 patients as LOPDD. We excluded patients with focal neurological deficits, evidence of focal brain lesions, diffuse white matter intensities, multiple lacunae in the basal ganglia based on conventional MRI, or other past medical comorbidities that could contribute to cognitive decline.

Controls

We recruited 121 healthy age- and sex-matched normal controls who had no history of neurological disease and no abnormalities on neurologic examinations. The normal controls exhibited no objective cognitive dysfunction on the Mini-Mental State Examination (MMSE) and the SNSB. The normal controls were divided into the young control group (<70 years of age) and the old control group (≥70 years of age) and were compared with the respective age-matched PDD group; 49 were allotted to the young controls and 72 to the old controls. Among control subjects, MRI scans for imaging analysis were available in 91 subjects; 39 were allocated to the young control group and 51 to the old control group.

Standard protocol approvals, registrations, and patient consents

We received approval from 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 data set was obtained from all subjects using a 3D 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°; slice gap, 0 mm.

Image processing for cortical thickness

We corrected image intensity inhomogeneity caused by magnetic field inhomogeneity by varying the signal intensity slowly over the image. The N3 algorithm was used to correct images for intensity non-uniformities resulting from inhomogeneities in the magnetic field [13]. Cortical thickness was measured on T1-weighted image by using the fully automated CIVET pipeline [14]. Skull stripping was performed using a Brain Extraction Tool with a deformable model fitted to the brain surface and optimized parameters [15]. Each brain was transformed separately into a standardized stereotaxic space (an ICBM 152 template) and resampled on a 1 mm³ voxel grid to account for inter-individual differences in absolute brain size [16]. An artificial neural network classifier that was validated in human brain images, was applied to identify gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) [17]. Partial volume level and MRI intensity mixing at the tissue interfaces due to the finite resolution of the imaging device were estimated and corrected using a trimmed minimum covariance determinant method [18]. A cortical surface was extracted automatically from each MR volume using the Constrained Laplacian-based Automated Segmentation with Proximities algorithm [19]. Cortical thickness was defined as the Euclidean distance between linked vertices or the inner and outer surfaces. The inner surface was defined by the WM/GM boundary surfaces and the outer surface by GM/CSF boundary surfaces. Then, diffusion smoothing, which generalizes Gaussian kernel smoothing, with 20 mm FWHM (full width half maximum) was used to increase the signal to noise ratio and detect population changes very well. To assess differences in cortical thickness between the PDD group and the control group, a general linear model was constructed with age, sex, and intracranial volume as independent variables and each of the vertices of thickness as a dependent variable. For multiple comparisons, the results were thresholded at a false discovery rate (FDR)-corrected p-value of 0.05 and cluster of 100. In addition, the effect of diagnosis (PDD versus control), age (<70 years versus ≥ 70 years), and their interaction with cortical thinning was assessed using analysis of covariance (ANCOVA) with a threshold of uncorrected p < 0.001 and cluster of 100, controlling for sex, educational level, hypertension, diabetes mellitus, and hyperlipidemia.

Volumetric determination of SI

The analysis was performed using Statistical Parametric Mapping software (SPM8, Wellcome Department of Imaging Neuroscience, London, UK). Each structural MRI scan was bias-corrected, segmented into SPM default tissue probability maps, and then normalized with VBM8 DARTEL templates for registration to MNI space using linear and nonlinear transformations within a unified model. According to a previous study [20], the ROI of SI was defined for the left and right hemispheres based on the location of the anterior commissure, which forms the boundary of the superior part of the end of the anterior third of the SI. The ROI extended 25 mm lateral from the midline, 13 mm ventral from the superior edge of the anterior commissure at the midline, and 3 mm anterior and 9 mm posterior from the middle of the anterior commissure (Supplementary Figure 1). The masks were created using the WFU PickAtlas 2.4 software [21], and volumetry of GM within a selected ROI was performed automatically. To correct for individual brain size, volumes were normalized by dividing by total intracranial volume derived from the masks covering the entire brain. Normalized SI volume was defined by the following formula: total SI volume (mL)/total intracranial volume (mL) X 10,000.

¹⁸F-FP-CIT PET and analysis of data

¹⁸F-FP-CIT PET scans were performed with a GE PET-CT DSTe scanner (GE Discovery STE, GE Healthcare Technologies, Milwaukee, WI), which obtains images with 3D resolution of 2.3 mm full width at half maximum. Quantitative analyses were performed following a modified version of a previously described procedure [22]. Quantitative analyses were based on volumes of interest (VOIs), which were defined based on a template in standard space. All reconstructed PET images were spatially normalized to Talairach space using a standard ¹⁸F-FP-CIT PET template which was made using ¹⁸F-FP-CIT PET and T1 MR images of 13 normal controls to remove inter-subject anatomical variability. The 8 VOIs of bilateral striatal subregions and one occipital VOI were drawn on a coregistered spatially normalized single T1 MR and 18F-FPCIT PET template image. The striatum was divided into the caudate, ventral striatum, anterior putamen, and posterior putamen. The VOI for the ventral striatum was defined according to previously defined criteria [23], and the boundary between the anterior and posterior putamen was the anterior commissure coronal plane. Dopamine transporter (DAT) activity was calculated by the non-displaceable binding potential, which was defined as follows: (mean standardized uptake value of the striatal subregions VOI— mean standardized uptake value of the occipital VOI)/mean standardized uptake value of the occipital VOI [24].

Statistical analysis

The χ² and Mann-Whitney U tests were used for categorical and continuous variables, respectively. The effects of diagnosis (PDD versus control), age (<70 years versus ≥ 70 years), and their interaction on neuropsychological tests and volumes of SI were assessed using ANCOVA, controlling for sex,educational duration, hypertension, diabetes mellitus, and hyperlipidemia. The significance of the interaction was tested in a full factorial model including the two main effects of diagnosis and age and their interaction. Pearson’s correlation analysis was used to evaluate the relationship between normalized SI volume and cognitive performance or cortical thickness. To compare Pearson’s correlation coefficients between young-aged group and old-aged groups, we used Fisher r-to-z transformation through correction of deviations from normal distribution. Statistical analyses were performed using commercially available software (SPSS, version 18.0), and a two-tailed p < 0.05 was considered to indicate statisticalsignificance.

RESULTS

Demographic characteristics of the EOPDD and LOPDD

The demographic characteristics of patients with PDD are shown in Table 1. Mean age at onset was 64.6 years in EOPDD and 75.5 years in LOPDD. There were no significant differences in sex, educational level, duration of Parkinsonism, and duration of cognitive impairment between the EOPDD and LOPDD groups. K-MMSE score tended to be higher in the EOPDD group (19.9) than the LOPDD group (18.5), which may be due to the difference in age. Levodopa equivalent dose and vascular risk factors, such as hypertension, diabetes mellitus, or dyslipidemia, did not significantly differ between the EOPDD and LOPDD groups. The UPDRS motor score was significantly higher in patients with LOPDD (35.5) than EOPDD (28.8, p = 0.005). EOPDD patients and LOPDD patients group did not show significant differences between their control groups in age, sex, K-MMSE score, education duration, and vascular risk factors.

The demographic characteristics of 65 patients with available MRI data were similar to those of patients with PDD (Supplementary Table 1). Mean age at onset in these 65 patients was 64.5 years in the EOPDD group and 74.7 years in the LOPDD group. No significant differences in sex, MMSE, educational level, PD duration, levodopa equivalent dose, UPDRS motor score, or duration of memory complaints were observed between the EOPDD and LOPDD groups. Total intracranial volume was not statistically different between the two patient groups (p = 0.596).

Neuropsychological features of the EOPDD and LOPDD

The neuropsychological features are shown in Table 2. Compared with the young control group, EOPDD patients showed significantly poorer performances in all domains of attention, language, visuospatial, and memory functions. Similarly, the LOPDD group showed a lower score in all cognitive subdomains compared with the old control group. The results of a direct comparison between the EOPDD and LOPDD groups in terms of an interaction between diagnosis and age are illustrated in Table 3. Significant group-by-age interaction effects were observed for digit backward and digit forward span test (p = 0.011 and 0.05, respectively) and visual recognition memory function (p = 0.012) in EOPDD patients compared with LOPDD patients. In addition, EOPDD patients tended to show poorer performance in phonemic generative naming, contrasting program, and verbal memory tests (p = 0.095, 0.078, and 0.118, respectively) than the LOPDD patients, although the difference was not statistically significant. No significant interaction effects between EOPDD and LOPDD groups were observed in language, visuospatial function, or other frontal-executive functions.

The group-by-age interaction effects in the PDD subpopulation with available MRI data are illustrated in Supplementary Table 3. Similar to the overall PDD patients, significant group-by-age interaction effects were observed for backward digit span and visual recognition memory tests (p = 0.009 and 0.006, respectively) in EOPDD patients compared with LOPDD patients.

Cortical thickness analysis between the EOPDD and LOPDD groups

A comparison of cortical thickness between EOPDD patients and age-matched controls is shown in Fig. 1A. As expected, patients with EOPDD exhibited a cortical thinning in extensive cortical areas, involving frontal, temporal, and parietal areas. A comparison of LOPDD and age-matched controls is shown in Fig. 1B. The LOPDD group showedsignificant decrease in cortical thickness in widespread cortical areas of frontal, temporal, and parietal areas, with prominent cortical thinning in bilateral prefrontal areas. A comparison of cortical thickness between the EOPDD and LOPDD groups based on interaction effect demonstrated that, compared with LOPDD patients, EOPDD patients exhibited decrease in cortical thickness in the left anterior cingulate gyrus and a small area of the right inferior temporal gyrus when adjusting for age with a threshold of uncorrected p < 0.001 (Fig. 1C).

Comparison of SI volume between the EOPDD and LOPDD groups

The mean normalized SI volumes among groups are shown in Table 4. Compared with the young control group, the mean normalized SI volume was significantly decreased in EOPDD patients (p < 0.001). In LOPDD patients, the mean normalized SI was decreased compared to the control group, although the difference was not statistically significant. A significant group-by-age interaction effect was observed, suggesting that SI volume was relatively decreased in the EOPDD group comparing to the LOPDD group after adjusting for the aging effect (p = 0.044).

Correlation between the normalized SI volume and performance in cognitive subdomains or cortical thickness in young age and old age groups

The correlation analysis between the normalized SI volume and cognitive performance in each group is shown in Supplementary Table 3. In young age group, the normalized SI volume was significantly positively correlated with all subdomains of cognitive test, whereas it was not correlated with all subdomains except letter cancellation test (p = 0.012) in old age group. A comparison of correlation coefficients between the groups and individual cognitive subdomains revealed that correlation coefficients of EOPDD group were significantly higher in the backward digit spans (p = 0.02), interlocking pentagon (p = 0.05), visual memory (p = 0.01), phonemic generative (p = 0.05), and color Stroop test (p = 0.04). In analysis between the normalized SI volume and cortical thickness, no significant clusters that were significantly correlated were observed in either patients with EOPDD or LOPDD.

DAT activity in ¹⁸F-FP-CIT PET

Comparison of ¹⁸F-FP-CIT uptakes in the striatal subregions between the groups is shown in Supplementary Table 4. DAT activity did not differ between patients with EOPDD and LOPDD in all striatal subregions.

DISCUSSION

The present study demonstrated for the first time that EOPDD patients had a poorer cognitive performance on attention and visual recognition memory tests after adjusting for aging effects compared with LOPDD patients. In addition, an analysis of cortical thickness and SI volume between the EOPDD and LOPDD groups based on interaction effect showed that EOPDD patients exhibited cortical thinning in the left anterior cingulate gyrus and a small area of the right inferior temporal gyrus as well as a smaller SI volume than the LOPDD patients. Our data suggest that EOPDD patients exhibit poorer cognitive performance and more severe atrophy in the cortex and SI compared with LOPDD patients.

Neuropsychological tests showed that EOPDD patients had poorer performance on attention tests compared with LOPDD patients. Importantly, attention is a hallmark of PD-related cognitivedysfunction; thus, underlying impaired attention in PD is a key factor contributing to the development of cognitive fluctuation, visual hallucinating, frontal executive dysfunction, or visuospatial dysfunction. Impaired attention has also been associated with a more rapid cognitive decline in patients with PD [3]. Of neuroanatomical correlates responsible for attention, the cholinergic system arising from the SI is closely associated with attention in patients with PD [25]. Evidence has shown that the SI undergoes degeneration in the early stages of PD [26], with its volume being a significant predictor of PDD [7], and patients with PDD have a profound cholinergic deficit compared with PD patients without cognitive deficits [6]. In the present study, the EOPDD group showed a significant volume reduction in the SI compared with the LOPDD group, which may be associated with attention deficits in the EOPDD patients. Moreover, the SI volume is more significantly correlated with cognitive performance in the EOPDD group relative to the LOPDD group, suggesting that cholinergic deficit may have an important role in developing cognitive decline in patients with EOPDD. In addition, patients with EOPDD demonstrated cortical thinning in the anterior cingulate gyrus compared with LOPDD patients. This area, along with medial prefrontal cortex, involves in various cognitive and motor process such as conflict monitoring [27], error detection [28], adaptive control [29], memory [30], and motor timing in PD by dopaminergic modulation [31] as well as attention. Generally, neural substrates of attention control are extensive networks of regions that include prefrontal and parietal cortices, posterior parietal cortex, and cingulate cortex [32, 33]. Of those, the anterior cingulate gyrus is reciprocally connected with frontoparietal regions implicated in cognitive control and maintenance of goals [34]. Particularly, the anterior cingulate gyrus plays a role in identifying the motivational relevance of extrapersonal events and in sustaining the level of effort needed for execution of attentional tasks in terms of “top-down” attentional control [35, 36], thus being a critical component of an integrated network for modulation of directed attention. Taken together, these results suggest that greater atrophy in the SI and the anterior cingulate gyrus is possibly due to increased pathological burden in EOPDD compared with LOPDD and may lead to attention deficits in EOPDD patients.

Along with cholinergic network, dopaminergic input arising from fronto-striatal network is an important contributor of cognitive performance in patients with PD [37]. Especially, executive dysfunction was associated with prefrontal cortex, striatum, and dopamine dependent cortico-striatal loop [38]. Previous studies showed striatal dopamine depletion disrupting transmission in the fronto-striatal network was the determining factor in executive impairment in PD rather than frontal dysfunction itself [39]. In the present study, we demonstrated that striatal presynaptic dopamine depletion did not differ between EOPDD and LOPDD by quantifying ¹⁸F-FP-CIT PET data, suggesting that striatal dopaminergic network may be less important factor distinguishing EOPDD from LOPDD than cholinergic network.

We also found that EOPDD patients showed poorer performance on visual recognition memory tests. Inferior temporal gyrus is well known to be a part of ventral visual pathway involved in visual perception [40]. Many previous neuroimaging studies showed that this region is closely related to recognition of patterns based on visual categories [41, 42]. In terms of neuroanatomical correlates, the present study results suggest that cortical thinning patterns involving the right inferior temporal gyrus observed in patients with EOPDD compared with LOPDD may be attributed to visual recognition memory dysfunction.

In cortical thickness analysis, the EOPDD and LOPDD groups showed cortical thinning in bilateral frontotemporoparietal areas compared with controls; however, the LOPDD group showed more widespread cortical thinning involving entire cortical areas, more significantly in the prefrontal cortex. Considering the extent of cortical thinning in the LOPDD group compared with controls, the major contributor of cortical thinning between the EOPDD and LOPDD patients is the aging process. This is consistent with a previous study of cortical thinning in aging, showing prominent prefrontal thinning and relative sparing of temporal and parahippocampal cortices [43]. However, when adjusting for the aging effect, the present study showed that the EOPDD group exhibited significant cortical thinning in the left anterior cingulate gyrus and right inferior temporal gyrus as well as smaller SI volume than the LOPDD group. Interestingly, these appear to be key areasvulnerable to development of dementia in PD. Patients with PD exhibited a more accelerated rate of cortical thinning in the bilateral frontotemporal areas compared with controls, representing evolving patterns of PD-related degenerative changes [44, 45]. Moreover, a longitudinal neuroimaging study demonstrated that cortical thinning in anterior cingulate area and the superior frontal area is a significant baseline predictor of development of dementia [46]. Additionally, our previous study demonstrated that the baseline volume of the SI is a significant predictor of PDD [7]. Accordingly, these results imply that anatomical correlates related to development of dementia in patients with PD may exhibit a similar contribution to the development of EOPDD. However, considering that posterior cortical atrophy or hypometabolism is also an important factor in PDD [1, 4], posterior cortical lobe-based pathologies may exert minimal influence on EOPDD.

Pathologic heterogeneity may also contribute to the clinical heterogeneity of PDD in terms of chronological aspects. The pathological study showed that cortical Lewy bodies are the best pathological correlates of dementia in PD [47]; however, other pathologies also act as important modulators in the process of cognitive decline in PD. For example, concurrent AD pathology appears to be significantly correlated with moderate to severe dementia [48] and closely associated with faster progression to dementia in patients with PDD [49]. Importantly, Irwin et al. [50] recently showed that a large number of PDD patients demonstrated AD pathologies at a higher correlation with older age in PD, suggesting that LOPDD patients are at greater risk of comorbid AD. Together, given temporoparietal patterns of cortical thinning as a marker of AD pathology [51, 52], it is possible that comorbid AD in EOPDD patients may be less of a risk, and PD pathological burden may contribute purely to the development of EOPDD. A future study using clinicopathological correlation in patients with earlier cognitive dysfunction would advance the understanding of pathobiology of cognitive decline in PD patients.

This study had several limitations. In contrast to AD, where an arbitrary cutoff age of 65 years widely used, there is no consensus on cutoff age regarding to earlier progression of PDD. However, observational study showed a higher prevalence of PDD in patients whose disease had begun after age 70 years [53]. Additionally, clinico-pathologic studies in patients with PD showed accelerated rate of deterioration in cognitive performance after 70 years [1, 8],suggesting that age of 70 years might be reasonable cutoff age. Nevertheless, this arbitrary cutoff age may act as one of confounding factor. Second, this study was not based on autopsy-proven data, and thus, we cannot draw a solid conclusion regarding pathological substrates responsible for EOPDD. Third, in cortical thickness analysis, significant clusters were observed using a relatively liberal threshold; therefore, we could not exclude the possibility of false positives. Other imaging analyses used to evaluate microstructural abnormality or functional network changes with a large sample size are warranted to resolve this issue. In vivo imaging of cerebral acetylcholinesterase is also required to perform a direct comparison of cortical cholinergic activity between the groups. Finally, due to this study’s cross-sectional design, a longitudinal change in cognitive performance between the EOPDD and LOPDD groups is lacking.

In summary, our data suggest that EOPDD patients have poorer cognitive performance and more severe cortical and SI volume loss compared LOPDD patients. These data suggest that PD-related pathological burden responsible for dementia in PD patients may be greater in EOPDD patients, thus implying that EOPDD may be a distinct phenotype different from LOPDD.

Footnotes

ACKNOWLEDGMENTS

This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI14C0093).

Authors’ disclosures available online ().

Appendix

References

Williams-Gray

, Foltynie

, Brayne

, Robbins

, Barker

(2007) Evolution of cognitive dysfunction in an incident Parkinson’s disease cohort. Brain 130, 1787–1798.

Janvin

, Aarsland

, Larsen

(2005) Cognitivepredictors of dementia in Parkinson’s disease: A community-based, 4-year longitudinal study. J Geriatr Psychiatry Neurol 18, 149–154.

Taylor

, Rowan

, Lett

, O’Brien

, McKeith

, Burn

(2008) Poor attentional function predicts cognitive decline in patients with non-demented Parkinson’s disease independent of motor phenotype. J Neurol Neurosurg Psychiatry 79, 1318–1323.

Bohnen

, Koeppe

, Minoshima

, Giordani

, Albin

, Frey

, Kuhl

(2011) Cerebral glucose metabolic features of Parkinson disease and incident dementia: Longitudinal study. J Nucl Med 52, 848–855.

Yarnall

, Rochester

, Burn

(2011) The interplay of cholinergic function, attention, and falls in Parkinson’sdisease. Mov Disord 26, 2496–2503.

Bohnen

, Kaufer

, Ivanco

, Lopresti

, Koeppe

, Davis

, Mathis

, Moore

, DeKosky

(2003) Cortical cholinergic function is more severely affected in parkinsonian dementia than in Alzheimer disease: an in vivo positron emission tomographic study. Arch Neurol 60, 1745–1748.

Lee

, Cho

, Song

, Kim

, Lee

, Sohn

, Lee

(2014) Exploratory analysis of neuropsychological and neuroanatomical correlates of progressive mild cognitive impairment in Parkinson’s disease. J Neurol Neurosurg Psychiatry 85, 7–16.

Kempster

, Williams

, Selikhova

, Holton

, Revesz

, Lees

(2007) Patterns of levodopa response inParkinson’s disease: a clinico-pathological study. Brain 130, 2123–2128.

Wickremaratchi

, Ben-Shlomo

, Morris

(2009) The effect of onset age on the clinical features of Parkinson’s disease. Eur J Neurol 16, 450–456.

10.

Hughes

, Daniel

, Kilford

, Lees

(1992) Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry 55, 181–184.

11.

Emre

, Aarsland

, Brown

, Burn

, Duyckaerts

, Mizuno

, Broe

, Cummings

, Dickson

, Gauthier

, Goldman

, Goetz

, Korczyn

, Lees

, Levy

, Litvan

, McKeith

, Olanow

, Poewe

, Quinn

, Sampaio

, Tolosa

, Dubois

(2007) Clinical diagnostic criteria for dementia associated with Parkinson’s disease. Mov Disord 22, 1689–1707.

12.

Kang

YW ND

(2003) Seoul Neuropsychological Screening Battery, Human Consulting Co., Incheon.

13.

Sled

, Zijdenbos

, Evans

(1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17, 87–97.

14.

Lyttelton

, Boucher

, Robbins

, Evans

(2007) An unbiased iterative group registration template for cortical surface analysis. Neuroimage 34, 1535–1544.

15.

Smith

(2002) Fast robust automated brain extraction. Hum Brain Mapp 17, 143–155.

16.

Mazziotta

, Toga

, Evans

, Fox

, Lancaster

(1995) A probabilistic atlas of the human brain: theory and rationale for its develoment. The International Consortium for Brain Mapping (ICBM). Neuroimage 2, 89–101.

17.

Zijdenbos

, Forghani

, Evans

(2002) Automatic “pipeline” analysis of 3-D MRI data for clinical trials: Application to multiple sclerosis. IEEE Trans Med Imaging 21, 1280–1291.

18.

Tohka

, Zijdenbos

, Evans

(2004) Fast and robust parameter estimation for statistical partial volume models in brain MRI. Neuroimage 23, 84–97.

19.

Kim

, Singh

, Lee

, Lerch

, Ad-Dab’bagh

, MacDonald

, Lee

, Kim

, Evans

(2005) Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. Neuroimage 27, 210–221.

20.

Teipel

, Flatz

, Heinsen

, Bokde

, Schoenberg

, Stockel

, Dietrich

, Reiser

, Moller

, Hampel

(2005) Measurement of basal forebrain atrophy in Alzheimer’s disease using MRI. Brain 128, 2626–2644.

21.

Maldjian

, Laurienti

, Kraft

, Burdette

(2003) An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage 19, 1233–1239.

22.

, Kim

, Shin

, Park

, Kim

, Moon

, Oh

, Chung

, Lee

(2012) Subregional patterns of preferential striatal dopamine transporter loss differ in Parkinson disease, progressive supranuclear palsy, and multiple-system atrophy. J Nucl Med 53, 399–406.

23.

Mawlawi

, Martinez

, Slifstein

, Broft

, Chatterjee

, Hwang

, Huang

, Simpson

, Ngo

, Van Heertum

, Laruelle

(2001) Imaging human mesolimbic dopamine transmission with positron emission tomography: I. Accuracy and precision of D(2) receptor parameter measurements in ventral striatum. J Cereb Blood Flow Metab 21, 1034–1057.

24.

Innis

, Cunningham

, Delforge

, Fujita

, Gjedde

, Gunn

, Holden

, Houle

, Huang

, Ichise

, Iida

, Ito

, Kimura

, Koeppe

, Knudsen

, Knuuti

, Lammertsma

, Laruelle

, Logan

, Maguire

, Mintun

, Morris

, Parsey

, Price

, Slifstein

, Sossi

, Suhara

, Votaw

, Wong

, Carson

(2007) Consensus nomenclature for in vivo imaging of reversibly binding radioligands. J Cereb Blood Flow Metab 27, 1533–1539.

25.

Perry

, Curtis

, Dick

, Candy

, Atack

, Bloxham

, Blessed

, Fairbairn

, Tomlinson

, Perry

(1985) Cholinergic correlates of cognitive impairment in Parkinson’s disease: comparisons with Alzheimer’s disease. J Neurol Neurosurg Psychiatry 48, 413–421.

26.

Nakano

, Hirano

(1984) Parkinson’s disease: neuron loss in the nucleus basalis without concomitant Alzheimer’s disease. Ann Neurol 15, 415–418.

27.

Botvinick

, Cohen

, Carter

(2004) Conflict monitoring and anterior cingulate cortex: An update. Trends Cogn Sci 8, 539–546.

28.

Narayanan

, Laubach

(2008) Neuronal correlates of post-error slowing in the rat dorsomedial prefrontal cortex. J Neurophysiol 100, 520–525.

29.

Euston

, Gruber

, McNaughton

(2012) The role of medial prefrontal cortex in memory and decision making. Neuron 76, 1057–1070.

30.

Rugg

, Fletcher

, Frith

, Frackowiak

, Dolan

(1996) Differential activation of the prefrontal cortex in successful and unsuccessful memory retrieval. Brain 119(Pt 6), 2073–2083.

31.

Jahanshahi

, Jones

, Zijlmans

, Katzenschlager

, Lee

, Quinn

, Frith

, Lees

(2010) Dopaminergic modulation of striato-frontal connectivity during motor timing in Parkinson’s disease. Brain 133, 727–745.

32.

Kastner

, Ungerleider

(2000) Mechanisms of visual attention in the human cortex. Annu Rev Neurosci 23, 315–341.

33.

Vandenberghe

, Duncan

, Dupont

, Ward

, Poline

, Bormans

, Michiels

, Mortelmans

, Orban

(1997) Attention to one or two features in left or right visual field: A positron emission tomography study. J Neurosci 17, 3739–3750.

34.

Shackman

, Salomons

, Slagter

, Fox

, Winter

, Davidson

(2011) The integration of negative affect, pain and cognitive control in the cingulate cortex. Nat Rev Neurosci 12, 154–167.

35.

Gitelman

, Nobre

, Parrish

, LaBar

, Kim

, Meyer

, Mesulam

(1999) A large-scale distributed network for covert spatial attention: Further anatomical delineation based on stringent behavioural and cognitive controls. (Pt 6). Brain 122, 1093–1106.

36.

Mesulam

(1999) Spatial attention and neglect: parietal, frontal and cingulate contributions to the mental representation and attentional targeting of salient extrapersonal events. Philos Trans R Soc Lond B Biol Sci 354, 1325–1346.

37.

Zgaljardic

, Borod

, Foldi

, Mattis

(2003) A review of the cognitive and behavioral sequelae of Parkinson’sdisease: relationship to frontostriatal circuitry. Cogn Behav Neurol 16, 193–210.

38.

Lewis

, Dove

, Robbins

, Barker

, Owen

(2003) Cognitive impairments in early Parkinson’s disease are accompanied by reductions in activity in frontostriatal neural circuitry. J Neurosci 23, 6351–6356.

39.

Owen

, Doyon

, Dagher

, Sadikot

, Evans

(1998) Abnormal basal ganglia outflow in Parkinson’s disease identified with PET. Implications for higher cortical functions. Brain 121(Pt 5), 949–965.

40.

Ungerleider

, Haxby

(1994) ‘What’ and ‘where’ in the human brain. Curr Opin Neurobiol 4, 157–165.

41.

Herath

, Kinomura

, Roland

(2001) Visual recognition: evidence for two distinctive mechanisms from a PET study. Hum Brain Mapp 12, 110–119.

42.

Ishai

, Ungerleider

, Martin

, Schouten

, Haxby

(1999) Distributed representation of objects in the human ventral visual pathway. Proc Natl Acad Sci U S A 96, 9379–9384.

43.

Salat

, Buckner

, Snyder

, Greve

, Desikan

, Busa

, Morris

, Dale

, Fischl

(2004) Thinning of the cerebral cortex in aging. Cereb Cortex 14, 721–730.

44.

Ibarretxe-Bilbao

, Junque

, Segura

, Baggio

, Marti

, Valldeoriola

, Bargallo

, Tolosa

(2012) Progression of cortical thinning in early Parkinson’s disease. Mov Disord 27, 1746–1753.

45.

Mak

, Su

, Williams

, Firbank

, Lawson

, Yarnall

, Duncan

, Owen

, Khoo

, Brooks

, Rowe

, Barker

, Burn

, O’Brien

(2015) Baseline and longitudinal grey matter changes in newly diagnosed Parkinson’s disease: ICICLE-PD study. Brain 138, 2974–2986.

46.

Compta

, Pereira

, Rios

, Ibarretxe-Bilbao

, Junque

, Bargallo

, Camara

, Buongiorno

, Fernandez

, Pont-Sunyer

, Marti

(2013) Combined dementia-risk biomarkers in Parkinson’s disease: a prospective longitudinal study. Parkinsonism Relat Disord 19, 717–724.

47.

Braak

, Rub

, Jansen Steur

, Del Tredici

, de Vos

(2005) Cognitive status correlates with neuropathologic stage in Parkinson disease. Neurology 64, 1404–1410.

48.

Jellinger

, Seppi

, Wenning

, Poewe

(2002) Impact of coexistent Alzheimer pathology on the natural history of Parkinson’s disease. J Neural Transm 109, 329–339.

49.

Sabbagh

, Adler

, Lahti

, Connor

, Vedders

, Peterson

, Caviness

, Shill

, Sue

, Ziabreva

, Perry

, Ballard

, Aarsland

, Walker

, Beach

(2009) Parkinson disease with dementia: comparing patients with and without Alzheimer pathology. Alzheimer Dis Assoc Disord 23, 295–297.

50.

Irwin

, White

, Toledo

, Xie

, Robinson

, Van Deerlin

, Lee

, Leverenz

, Montine

, Duda

, Hurtig

, Trojanowski

(2012) Neuropathologic substrates of Parkinson disease dementia. Ann Neurol 72, 587–598.

51.

Whitwell

, Jack

Jr. , Przybelski

, Parisi

, Senjem

, Boeve

, Knopman

, Petersen

, Dickson

, Josephs

(2011) Temporoparietal atrophy: A marker of AD pathology independent of clinical diagnosis. Neurobiol Aging 32, 1531–1541.

52.

Weintraub

, Dietz

, Duda

, Wolk

, Doshi

, Xie

, Davatzikos

, Clark

, Siderowf

(2012) Alzheimer’s disease pattern of brain atrophy predicts cognitive decline in Parkinson’s disease. Brain 135, 170–180.

53.

Reid

, Hely

, Morris

, Broe

, Adena

, Sullivan

, Williamson

(1996) A longitudinal of Parkinson’s disease: Clinical and neuropsychological correlates of dementia. J Clin Neurosci 3, 327–333.

Cognitive and Neuroanatomical Correlates in Early Versus Late Onset Parkinson’s Disease Dementia

Abstract

Keywords

INTRODUCTION

MATERIALS AND METHODS

Subjects

Controls

Standard protocol approvals, registrations, and patient consents

MRI acquisition

Image processing for cortical thickness

Volumetric determination of SI

18F-FP-CIT PET and analysis of data

Statistical analysis

RESULTS

Demographic characteristics of the EOPDD and LOPDD

Neuropsychological features of the EOPDD and LOPDD

Cortical thickness analysis between the EOPDD and LOPDD groups

Comparison of SI volume between the EOPDD and LOPDD groups

Correlation between the normalized SI volume and performance in cognitive subdomains or cortical thickness in young age and old age groups

DAT activity in 18F-FP-CIT PET

DISCUSSION

Footnotes

ACKNOWLEDGMENTS

Appendix

References

¹⁸F-FP-CIT PET and analysis of data

DAT activity in ¹⁸F-FP-CIT PET