2020
DOI: 10.1002/mdc3.13003
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White Matter Hyperintensities Mediate Impact of Dysautonomia on Cognition in Parkinson's Disease

Abstract: Background Patients with Parkinson's disease (PD) present with a broad spectrum of nonmotor features including autonomic disorders. More severe autonomic dysfunction in PD is associated with increased cognitive deficits. The presence of cerebral small‐vessel disease, measured by T2‐weighted magnetic resonance imaging white matter hyperintensity (WMH) burden, is also observed in patients with PD with faster cognitive decline. Objective To investigate whether baseline orthostatic hypotension and autonomic dysfun… Show more

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Cited by 39 publications
(24 citation statements)
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“…With great interest we read the article recently published by Dadar and colleagues 1 in Movement Disorders Clinical Practice. They report that in patients with early untreated de novo Parkinson's disease (PD), the interplay between dysautonomia and the burden of white matter hyperintensities (WMH) results in pronounced and accelerated cognitive decline.…”
mentioning
confidence: 99%
“…With great interest we read the article recently published by Dadar and colleagues 1 in Movement Disorders Clinical Practice. They report that in patients with early untreated de novo Parkinson's disease (PD), the interplay between dysautonomia and the burden of white matter hyperintensities (WMH) results in pronounced and accelerated cognitive decline.…”
mentioning
confidence: 99%
“…The fact that subcortical structures and WMHs have contrasting intensity profiles in T2w and FLAIR images (i.e., hypointense vs. hyperintense, respectively) and similar intensity profiles in T1w images (hypointense for both) makes it unlikely that they would be incorrectly classified as WMHs, as the WMH segmentations are mainly driven by the intensity profile in T2w and FLAIR sequences. Additionally, the WMH segmentation method used here has been developed and extensively validated for use in multi‐site and multi‐scanner studies, and has been previously used in several multi‐site datasets, including ADNI (Anor, Dadar, Collins, & Tartaglia, 2020; Dadar et al, 2019; Dadar et al, 2020; Dadar, Camicioli, Duchesne, Collins, & Initiative, 2020; Dadar, Gee, Shuaib, Duchesne, & Camicioli, 2020; Misquitta et al, 2018; Sanford et al, 2019). The training library used consists of manually segmented labels from the same dataset (ADNI), to ensure optimal classifier performance (Dadar, Maranzano, Misquitta, et al, 2017).…”
Section: Discussionmentioning
confidence: 99%
“…Additionally, CAD may contribute not only to physical but also cognitive fatigue in MS (Sander et al, 2017 ), as significant associations between cognitive decline and objective physiological markers of CAD such as reduced HRV (Sander et al, 2019 ) or blunted BP response to sustained handgrip testing (Niepel et al, 2013 ) have been highlighted. More importantly, prospective cohort studies of both healthy and neurological populations have suggested that OH arising from baroreflex and sympathetic dysfunction could lead to cognitive deterioration (e.g., through white matter lesions) as a result of chronic hypoperfusion and suboptimal cerebral autoregulation (Dadar et al, 2020 ; Zimmermann et al, 2020 ). Therefore, impairments of cognitive function, which are recognized as a risk factor for falls (Gunn et al, 2013 ; Sosnoff et al, 2013 ), may represent a further mediator of the relationship between CAD and fall-risk.…”
Section: Relationship Between Cad and Fallsmentioning
confidence: 99%