Cognitive Profiles and Hub Vulnerability in Parkinson's Disease

Lin, Sue‐Jin; Baumeister, Tobias R.; Garg, Saurabh; McKeown, Martin J.

doi:10.3389/fneur.2018.00482

Cited by 40 publications

(46 citation statements)

References 61 publications

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“…Untangling the effect of the neuro-degeneration of PD from the effects of regular aging is important for further understanding the functional connectivity in PD patients associated with cognitive deficits. A recent study support this, showing a strong effect of aging on PD patients' cognition (8).…”

Section: Introductionmentioning

confidence: 65%

“…A significant difference in age occurred between the PD-non-MCI and PD-MCI (Table 2). Matching for age of PD-non MCI and PD-MCI, however, might induce a recruitment bias, as many studies indicate that PD patients with MCI are generally older than PD-non MCI (2, 7, 8). Here, we opted for another strategy to investigate the effect of age, we added a young healthy group allowing us to investigate of age vs. the effect of disease.…”

Section: Methodsmentioning

confidence: 99%

“…Studies indicate that cognition in PD patients is associated not only with age, but also gender, depression, education, and severity of motor symptoms (7, 8). Accordingly, there also existed group difference between the PD-non-MCI and PD-MCI, in sex, and Beck's Depression Inventory II (BDI-II, as an index of depression), but not in education or UPDRS motor scores.…”

Section: Methodsmentioning

confidence: 99%

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Why Is Aging a Risk Factor for Cognitive Impairment in Parkinson's Disease?—A Resting State fMRI Study

et al. 2019

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Using resting-state functional MRI (rsfMRI) data of younger and older healthy volunteers and patients with Parkinson's disease (PD) with and without mild cognitive impairment (MCI) and applying two different analytic approaches, we investigated the effects of age, pathology, and cognition on brain connectivity. When comparing rsfMRI connectivity strength of PD patients and older healthy volunteers, reduction between multiple brain regions in PD patients with MCI (PD-MCI) compared with PD patients without MCI (PD-non-MCI) was observed. This group difference was not affected by the number and location of clusters but was reduced when age was included as a covariate. Next, we applied a graph-theory method with a cost-threshold approach to the rsfMRI data from patients with PD with and without MCI as well as groups of younger and older healthy volunteers. We observed decreased hub function (measured by degree and betweenness centrality) mainly in the medial prefrontal cortex (mPFC) in older healthy volunteers compared with younger healthy volunteers. We also found increased hub function in the posterior medial structure (precuneus and the cingulate cortex) in PD-non-MCI patients compared with older healthy volunteers and PD-MCI patients. Hub function in these posterior medial structures was positively correlated with cognitive function in all PD patients. Together these data suggest that overlapping patterns of hub modifications could mediate the effect of age as a risk factor for cognitive decline in PD, including age-related reduction of hub function in the mPFC, and recruitment availability of the posterior medial structure, possibly to compensate for impaired basal ganglia function.

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Section: Introductionmentioning

confidence: 65%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Why Is Aging a Risk Factor for Cognitive Impairment in Parkinson's Disease?—A Resting State fMRI Study

et al. 2019

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“…We note that sparse methods are also often used in classification tasks, where they have been observed to provide better prediction but less stable weights (55,56), which indicates a trade-off between prediction and inference (55). Correspondingly, it has been suggested to consider weight stability as a criterion in sparsity parameter selection (55,57,58).…”

Section: Brain-behavior Associationsmentioning

confidence: 99%

On stability of Canonical Correlation Analysis and Partial Least Squares with application to brain-behavior associations

Helmer

Warrington

Mohammadi‐Nejad

et al. 2020

Preprint

112

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Associations between high-dimensional datasets, each comprising many features, can be discovered through multivariate statistical methods, like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). CCA and PLS are widely used methods which reveal which features carry the association. Despite the longevity and popularity of CCA/PLS approaches, their application to high-dimensional datasets raises critical questions about the reliability of CCA/PLS solutions. In particular, overfitting can produce solutions that are not stable across datasets, which severely hinders their interpretability and generalizability. To study these issues, we developed a generative model to simulate synthetic datasets with multivariate associations, parameterized by feature dimensionality, data variance structure, and assumed latent association strength. We found that resulting CCA/PLS associations could be highly inaccurate when the number of samples per feature is relatively small. For PLS, the profiles of feature weights exhibit detrimental bias toward leading principal component axes. We confirmed these model trends in state-ofthe-art datasets containing neuroimaging and behavioral measurements in large numbers of subjects, namely the Human Connectome Project (n ≈ 1000) and UK Biobank (n = 20000), where we found that only the latter comprised enough samples to obtain stable estimates. Analysis of the neuroimaging literature using CCA to map brain-behavior relationships revealed that the commonly employed sample sizes yield unstable CCA solutions. Our generative modeling framework provides a calculator of dataset properties required for stable estimates. Collectively, our study characterizes dataset properties needed to limit the potentially detrimental effects of overfitting on stability of CCA/PLS solutions, and provides practical recommendations for future studies.Significance StatementScientific studies often begin with an observed association between different types of measures. When datasets comprise large numbers of features, multivariate approaches such as canonical correlation analysis (CCA) and partial least squares (PLS) are often used. These methods can reveal the profiles of features that carry the optimal association. We developed a generative model to simulate data, and characterized how obtained feature profiles can be unstable, which hinders interpretability and generalizability, unless a sufficient number of samples is available to estimate them. We determine sufficient sample sizes, depending on properties of datasets. We also show that these issues arise in neuroimaging studies of brain-behavior relationships. We provide practical guidelines and computational tools for future CCA and PLS studies.

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“…Different studies showed that male PD patients have worse general cognitive abilities and male sex is the primary predictive factor for mild cognitive impairment and its more rapid progression in the severe stage of the disease [26][27][28]. Accordingly, female PD patients perform better at the Symbol Digit Modalities Test, verbal fluency tests and overall cognition measured by the Montreal Cognitive Assessment scale, but present with worse visuospatial function [29]. Significant sex differences are also observed for frontal executive abilities (attention and working memory), with male PD patients showing greater deficits than female non-demented patients.…”

Section: Non-motor Symptomsmentioning

confidence: 99%

Parkinson’s Disease in Women and Men: What’s the Difference?

Cerri¹,

Mus²,

Blandini³

2019

Journal of Parkinson’s Disease

488

309

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Increasing evidence points to biological sex as an important factor in the development and phenotypical expression of Parkinson's disease (PD). Risk of developing PD is twice as high in men than women, but women have a higher mortality rate and faster progression of the disease. Moreover, motor and nonmotor symptoms, response to treatments and disease risk factors differ between women and men. Altogether, sex-related differences in PD support the idea that disease development might involve distinct pathogenic mechanisms (or the same mechanism but in a different way) in male and female patients. This review summarizes the most recent knowledge concerning differences between women and men in PD clinical features, risk factors, response to treatments and mechanisms underlying the disease pathophysiology. Unraveling how the pathology differently affect the two sexes might allow the development of tailored interventions and the design of innovative programs that meet the distinct needs of men and women, improving patient care.

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Cognitive Profiles and Hub Vulnerability in Parkinson's Disease

Cited by 40 publications

References 61 publications

Why Is Aging a Risk Factor for Cognitive Impairment in Parkinson's Disease?—A Resting State fMRI Study

Why Is Aging a Risk Factor for Cognitive Impairment in Parkinson's Disease?—A Resting State fMRI Study

On stability of Canonical Correlation Analysis and Partial Least Squares with application to brain-behavior associations

Parkinson’s Disease in Women and Men: What’s the Difference?

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