scholarly journals A meta‐analysis of neurocognition in mild cognitive impairment in Parkinson’s disease and progression to Parkinson’s disease dementia

2020 ◽  
Vol 16 (S6) ◽  
Author(s):  
Elizabeth Wallace ◽  
Kullen Balthrop ◽  
Jordan Harp ◽  
Suzanne Segerstrom ◽  
Craig van Horne ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Meta Analysis ◽  
Parkinson’S Disease Dementia

scholarly journals Neuroimaging in Parkinson’s disease dementia: connecting the dots

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Rimona S Weil ◽  
Joey K Hsu ◽  
Ryan R Darby ◽  
Louis Soussand ◽  
Michael D Fox
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Meta Analysis ◽  
Likelihood Estimation ◽  
Brain Network ◽  
Activation Likelihood Estimation ◽  
Stage Specificity ◽  
Network Mapping

Abstract Dementia is a common and devastating symptom of Parkinson’s disease but the anatomical substrate remains unclear. Some evidence points towards hippocampal involvement but neuroimaging abnormalities have been reported throughout the brain and are largely inconsistent across studies. Here, we test whether these disparate neuroimaging findings for Parkinson’s disease dementia localize to a common brain network. We used a literature search to identify studies reporting neuroimaging correlates of Parkinson’s dementia (11 studies, 385 patients). We restricted our search to studies of brain atrophy and hypometabolism that compared Parkinson’s patients with dementia to those without cognitive involvement. We used a standard coordinate-based activation likelihood estimation meta-analysis to assess for consistency in the neuroimaging findings. We then used a new approach, coordinate-based network mapping, to test whether neuroimaging findings localized to a common brain network. This approach uses resting-state functional connectivity from a large cohort of normative subjects (n = 1000) to identify the network of regions connected to a reported neuroimaging coordinate. Activation likelihood estimation meta-analysis failed to identify any brain regions consistently associated with Parkinson’s dementia, showing major heterogeneity across studies. In contrast, coordinate-based network mapping found that these heterogeneous neuroimaging findings localized to a specific brain network centred on the hippocampus. Next, we tested whether this network showed symptom specificity and stage specificity by performing two further analyses. We tested symptom specificity by examining studies of Parkinson’s hallucinations (9 studies, 402 patients) that are frequently co-morbid with Parkinson’s dementia. We tested for stage specificity by using studies of mild cognitive impairment in Parkinson’s disease (15 studies, 844 patients). Coordinate-based network mapping revealed that correlates of visual hallucinations fell within a network centred on bilateral lateral geniculate nucleus and correlates of mild cognitive impairment in Parkinson’s disease fell within a network centred on posterior default mode network. In both cases, the identified networks were distinct from the hippocampal network of Parkinson’s dementia. Our results link heterogeneous neuroimaging findings in Parkinson’s dementia to a common network centred on the hippocampus. This finding was symptom and stage-specific, with implications for understanding Parkinson’s dementia and heterogeneity of neuroimaging findings in general.

scholarly journals Predicting Future Cognitive Impairment in De Novo Parkinson's Disease Using Clinical Data and Structural MRI

2021 ◽  
Author(s):  
Nicola Smith ◽  
Owen A Williams ◽  
Lucia Ricciardi ◽  
Francesca Morgante ◽  
Thomas R Barrick ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Grey Matter ◽  
Temporal Cortex ◽  
Cognitive Status ◽  
Support Vector ◽  
Parkinson’S Disease Dementia ◽  
Lingual Gyrus

BACKGROUND Parkinson's disease is the second most common neurodegenerative condition and associated with increasing cognitive dysfunction as the disease progresses. However, subtle cognitive deficits can be detected at diagnosis in 42% of individuals, suggesting that damage may already be present. Our aim was to determine clinical and structural differences in those recently diagnosed with PD who later develop cognitive impairment, and whether these changes predict future cognitive decline. METHODS Clinical and imaging data was acquired from the Parkinson's Progression Markers Initiative for 318 individuals with a diagnosis of Parkinson's disease and baseline 3T T1-weighted MRI. The cohort was divided according to cognitive status over follow-up, with 9 individuals developing Parkinson's disease dementia, 102 developing mild cognitive impairment and 207 remaining cognitively unaffected. FINDINGS At baseline, those who went on to develop cognitive impairment (mild cognitive impairment or dementia) were older with more severe motor and non-motor symptoms (anosmia, rapid eye movement sleep behaviour disorder, depression). Grey matter loss was present in those destined for Parkinson's disease dementia in the precuneus, hippocampi, primary olfactory cortex, lingual gyrus, temporal cortex and cerebellum. Those who later developed mild cognitive impairment had an attenuated but similar pattern of grey matter loss in the temporal lobe, lingual gyrus and cerebellum. Using support vector machines with a feature selection step, future cognitive impairment could be predicted using 11 clinical variables (AUC = 0.81), structural imaging (AUC = 0.72) or a combination of these two modalities (AUC = 0.85). These models more accurately predicted those who developed dementia (subgroup sensitivity 100%). INTERPRETATION Significant abnormalities in cortical structure is present at least three years before dementia manifests in Parkinson's disease, with associated differences in clinical profiles. Combining this data provides a technique to accurately identify future cognitive impairment, providing a non-invasive way to stratify individuals early on.

scholarly journals Dynamic functional connectivity changes associated with dementia in Parkinson’s disease

Brain ◽  
2019 ◽  
Vol 142 (9) ◽  
pp. 2860-2872 ◽  
Author(s):  
Eleonora Fiorenzato ◽  
Antonio P Strafella ◽  
Jinhee Kim ◽  
Roberta Schifano ◽  
Luca Weis ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Connectivity ◽  
Parkinson’S Disease Dementia ◽  
Dynamic Functional Connectivity ◽  
Control Subjects ◽  
Healthy Control ◽  
Normal Cognition

AbstractDynamic functional connectivity captures temporal variations of functional connectivity during MRI acquisition and it may be a suitable method to detect cognitive changes in Parkinson’s disease. In this study, we evaluated 118 patients with Parkinson’s disease matched for age, sex and education with 35 healthy control subjects. Patients with Parkinson’s disease were classified with normal cognition (n = 52), mild cognitive impairment (n = 46), and dementia (n = 20) based on an extensive neuropsychological evaluation. Resting state functional MRI and a sliding-window approach were used to study the dynamic functional connectivity. Dynamic analysis suggested two distinct connectivity ‘States’ across the entire group: a more frequent, segregated brain state characterized by the predominance of within-network connections, State I, and a less frequent, integrated state with strongly connected functional internetwork components, State II. In Parkinson’s disease, State I occurred 13.89% more often than in healthy control subjects, paralleled by a proportional reduction of State II. Parkinson’s disease subgroups analyses showed the segregated state occurred more frequently in Parkinson’s disease dementia than in mild cognitive impairment and normal cognition groups. Further, patients with Parkinson’s disease dementia dwelled significantly longer in the segregated State I, and showed a significant lower number of transitions to the strongly interconnected State II compared to the other subgroups. Our study indicates that dementia in Parkinson’s disease is characterized by altered temporal properties in dynamic connectivity. In addition, our results show that increased dwell time in the segregated state and reduced number of transitions between states are associated with presence of dementia in Parkinson’s disease. Further studies on dynamic functional connectivity changes could help to better understand the progressive dysfunction of networks between Parkinson’s disease cognitive states.

scholarly journals Mild cognitive impairment as a risk factor for Parkinson's disease dementia

2017 ◽  
Vol 32 (7) ◽  
pp. 1056-1065 ◽  
Author(s):  
Jeroen Hoogland ◽  
Judith A. Boel ◽  
Rob M.A. de Bie ◽  
Ronald B. Geskus ◽  
Ben A. Schmand ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Risk Factor ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Parkinson’S Disease Dementia

Prevalence and Clinical Aspects of Mild Cognitive Impairment in Parkinson's Disease: A Meta‐Analysis

2019 ◽  
Vol 35 (1) ◽  
pp. 45-54 ◽  
Author(s):  
Chiara Baiano ◽  
Paolo Barone ◽  
Luigi Trojano ◽  
Gabriella Santangelo
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Meta Analysis ◽  
Clinical Aspects
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