Articles » ASL perfusion markers of cognitive status in Parkinson’s disease
ASL perfusion markers of cognitive status in Parkinson’s disease
To identify cerebral blood flow (CBF) perfusion patterns derived from Arterial Spin Labeling (ASL) MRI that characterize cognitive status in Parkinson's disease (PD).
Early diagnosis of Parkinson's disease (PD) is important for patient prognosis and optimum treatment selection. Quantitative CBF measurement has the potential to be
an important diagnostic biomarker in PD. Radiotracer studies by others have identified a specific PD-related metabolic/perfusion pattern characterized by pallidothalamic,
pontine, and motor cortex hyperperfusion, associated with a pattern of reduced perfusion in the lateral premotor, rostral supplementary motor, posterior parietal areas,
and prefrontal cortex.1-3 In this study, we applied quantitative, non-invasive ASL perfusion imaging4 to identify blood flow alterations in varying stages of cognitive status
Participants: Forty-four PD subjects (age±SD: 68.1±9.8 years, 34 males) and a group of 26 healthy controls matched for mean age, gender, and education (67.7±9.5 years, 18 males)
completed neuropsychological tests, motor assessment (UPDRS part 3), and MRI scans. The PD subjects were categorized as cognitively unimpaired (PDU; n = 22), mild cognitive impairment
(MCI; n = 13), and dementing (PDD; n = 9). Those with dementia were diagnosed on the basis of the Movement Disorders Task Force criteria;5 those with MCI did not show significant impairment
in functional activities of daily living but showed performance at 1.5 standard deviations or more below norms on at least two measures, in at least one of the four Task Force cognitive domains
(1. executive function; 2. memory; 3. attention, working memory and speed of processing; and 4. visuospatial/visuoperceptual function). Global cognitive status was assessed using the Mini
Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA).
Data Acquisition: We used pseudo-continuous ASL on a 3T GE HDx scanner to investigate CBF perfusion in PD and Controls (Figure 1). Whole-brain structural MR images were also
acquired. The images were preprocessed using statistical parametric mapping software SPM5 (Wellcome Department of Cognitive Neurology, University College London) and custom Matlab
scripts. The ASL-derived CBF map was coregistered to the structural image. Segmentation of the structural image using priors obtained from a probabilistic elderly brain atlas6 produced
normalization parameters. These parameters were then used to normalize the CBF map to a space which more accurately represented the ageing subject population. Images were smoothed
using an isotropic Gaussian kernel of 10mm.
Data Analysis: Principal component analysis (PCA) of the entire data set (PD and controls) resulted in a set of perfusion covariance patterns represented as principal component images.
The expression of the first component in each individual was used in a one-way analysis of variance (ANOVA: Control, PDU, MCI, and PDD) followed by Tukey's least significant difference
(LSD) post hoc test; all comparisons with p<0.05 were considered significant. Finally, correlation analyses examined the relationships between this component and MoCA (all participants) and
UPDRS part 3 (PD only).
Demographics: Table 1 summarizes gender, age, education, MoCA and UPDRS part 3 data for all participants.
Imaging: PD participants showed an increased expression (vs Controls) of the first component covariance pattern (Figure 2), characterized by relative hypoperfusion at
bilateral posterior parietal-occipital junction, extending anteriorly to include precentral and postcentral gyri, and superior/middle frontal cortex. Hypoperfusion also occurred
in the posterior cingulate gyrus, precuneus, bilateral superior/middle temporal lobe, left caudate and bilateral thalamus. ANOVA of the expression of the first component
(PCA1) produced a significant group effect (F3,66 = 4.99, p = 0.004, Figure 3a). Post hoc tests significantly differentiated PDD/Control (effect size: Cohen's d = 1.1),
MCI/Control (d = 1.1), and MCI/PDU (d = 0.8). PDD/MCI (d = 0.1) and PDU/Controls (d = 0.3) did not differ significantly. CBF values within the identified pattern decreased
steadily from Controls through PDU and MCI to reach their lowest value in the PDD group. There was no significant correlation with age or UPDRS part 3 (age: r = 0.08,
p = 0.49; UPDRS: r = 0.19, p = 0.24). MoCA score significantly correlated with the expression of the first component (MoCA: r = -0.35, p = 0.003; Figure 3b).
This study demonstrated that PD participants classified by cognitive impairment significantly differed from controls in terms of an increased expression of a supposed
PD-specific perfusion network. Radiotracer studies have identified regions of hyperperfusion in PD vs Control; this study only identified hypoperfusion.
The pattern was expressed to the greatest extent in PDD and MCI with reduction through PDU to Control. Decreased CBF was apparent in MCI, thus in many cases
MCI closely resembled PDD (as evident by the small effect size). Large effect sizes between PDD/Controls and MCI with PDU and Controls reaffirmed significant
differences. Correlation between global cognitive status (MoCA) and PCA1 suggests an association between cognition and perfusion in the identified areas with a lack of
motor involvement. Therefore, this ASL-derived perfusion pattern offers a potential biomarker and method of characterizing cognitive decline in PD.
The authors gratefully acknowledge support funding from the Neurological Foundation of New Zealand and Canterbury Medical Research Foundation.
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Tracy R. Melzer1,2, Richard Watts1,3, Michael R. MacAskill1,2, Ajit Shankaranarayanan4, David C. Alsop5, Ross Keenan6, Charlotte Graham1,2, Leslie Livingston1,2, John C. Dalrymple-Alford1,7, Tim J. Anderson1,2
1Van der Veer Institute for Parkinson's Disease and Brain Research, 2Department of Medicine, University of Otago, Christchurch, 3Department of Physics and Astronomy,
University of Canterbury, Christchurch, 4GE Healthcare, Menlo Park, California, USA, 5Beth Israel Deaconess Medical Center, Boston, MA, USA, 6Christchurch Radiology
Group, 7Department of Psychology, University of Canterbury, Christchurch, New Zealand