Utility of Diffusion Modeling of Cogstate Brief Battery Test Performance in Detecting Mild Cognitive Impairment

Cognitive testing data are essential to the diagnosis of mild cognitive impairment (MCI), and computerized cognitive testing, such as the Cogstate Brief Battery, has proven helpful in efficiently identifying harbingers of dementia. This study provides a side-by-side comparison of traditional Cogstate outcomes and diffusion modeling of these outcomes in predicting MCI diagnosis. Participants included 257 older adults (160 = normal cognition; 97 = MCI). Results showed that both traditional Cogstate and diffusion modeling analyses predicted MCI diagnosis with acceptable accuracy. Cogstate measures of recognition learning and working memory accuracy and diffusion modeling variable of decision-making efficiency (drift rate) and nondecisional time were most predictive of MCI. While participants with normal cognition demonstrated a change in response caution (boundary separation) when transitioning tasks, participants with MCI did not evidence this change.

Case Report: Progressive Asymmetric Parkinsonism Secondary to CADASIL Without Dementia

Parkinsonism is a rare phenotype of cerebral autosomal dominant arteriopathy with subcortical infarction and leukoencephalopathy (CADASIL), all of which involve cognitive decline. Normal cognition has not been reported in previous disease studies. Here we report the case of a 60-year-old female patient with a 2-year history of progressive asymmetric parkinsonism. On examination, she showed severe parkinsonism featuring bradykinesia and axial and limb rigidity with preserved cognition. Magnetic resonance imaging (MRI) revealed white matter hyperintensity in the external capsule and periventricular region. Dopaminergic response was limited. A missense mutation c.1630C>T (p.R544C) on the NOTCH3 gene was identified on whole-exome sequencing, which confirmed the diagnosis of vascular parkinsonism secondary to CADASIL. A diagnosis of CADASIL should be considered in asymmetric parkinsonism without dementia. Characteristic MRI findings support the diagnosis.

Explanatory role of sociodemographic, clinical, behavioral, and social factors on cognitive decline in older adults with diabetes

Background The aim of the study was to examine the explanatory role of sociodemographic, clinical, behavioral, and social factors on racial/ethnic differences in cognitive decline among adults with diabetes. Methods Adults aged 50+ years with diabetes from the Health and Retirement Survey were assessed for cognitive function (normal, mild cognitive impairment [MCI], and dementia). Generalized estimating equation (GEE) logistic regression models were used to account for repeating measures over time. Models were adjusted for sociodemographic (gender, age, education, household income and assets), behavioral (smoking), clinical (ie. comorbidities, body mass index), and social (social support, loneliness, social participation, perceived constraints and perceived mastery on personal control) factors. Results Unadjusted models showed non-Hispanic Blacks (NHB) and Hispanics were significantly more likely to progress from normal cognition to dementia (NHB OR: 2.99, 95%CI 2.35–3.81; Hispanic OR: 3.55, 95%CI 2.77–4.56), and normal cognition to MCI (NHB OR = 2.45, 95%CI 2.14–2.82; Hispanic OR = 2.49, 95%CI 2.13–2.90) compared to non-Hispanic Whites (NHW). Unadjusted models for the transition from mild cognitive decline to dementia showed Hispanics were more likely than NHW to progress (OR = 1.43, 95%CI 1.11–1.84). After adjusting for sociodemographic, clinical/behavioral, and social measures, NHB were 3.75 times more likely (95%CI 2.52–5.56) than NHW to reach dementia from normal cognition. NHB were 2.87 times more likely (95%CI 2.37–3.48) than NHW to reach MCI from normal. Hispanics were 1.72 times more likely (95%CI 1.17–2.52) than NHW to reach dementia from MCI. Conclusion Clinical/behavioral and social factors did not explain racial/ethnic disparities. Racial/ethnic disparities are less evident from MCI to dementia, emphasizing preventative measures/interventions before cognitive impairment onset are important.

Predictors of preoperative cognitive dysfunction in adults with Moyamoya disease: a preliminary research

Objective To explore potential risk factors of preoperative cognitive dysfunction in adult patients with moyamoya disease (MMD) and discuss significance of moyamoya vessels. Methods The author reviewed adult MMD patients harboring no parenchymal infarction or hemorrhage underwent a standardized neuropsychological assessment test battery from December 2018 to May 2019. The authors defined patients with cognitive dysfunction as cognitive impairment shown on 3 or more neuropsychological tests. According to the presence of cerebral angiography, arterial stenosis, moyamoya vessels, and compensatory arteries were conducted. Univariate and multivariate analyses were performed to identify predictors for cognitive dysfunction before surgery. Subgroup analyses by onset type and Suzuki stage were carried out to identify specific predictors for preoperative cognitive dysfunction. Results In total, 29 of 92 (31.52%) patients had cognitive dysfunction. Multivariate analysis showed that moyamoya vessels generating from left hemisphere was recognized as independent predictor for cognitive dysfunction (P = 0.025, OR [95%CI], 0.085 [0.012–0.874]). For patients in left ICA-moyamoya subgroup, 19 of 45 (42.22%) cases with sparse moyamoya vessels had cognitive dysfunction (P = 0.031), while 22 (91.67%) of patients with dense moyamoya vessels had normal cognition (P = 0.004). Moyamoya vessels arising from ophthalmic artery had no significant association with cognitive dysfunction (P = 0.111). Multivariate analysis found that moyamoya vessels originating from left ICA was recognized as independent predictors for preoperative cognitive dysfunction (P = 0.048, OR [95%CI], 0.394 [0.132–0.926]). Conclusions Moyamoya vessels arising from left hemisphere was a risk factor for the preoperative cognitive dysfunction in adult patients with MMD, with the denser moyamoya vessels, the less cognitive dysfunction. The current study offers a new perspective of moyamoya vessels and supporting data for choosing MMD candidates on cerebral revascularization.

Profiling Parkinson's disease cognitive phenotypes via resting-state magnetoencephalography

Aberrant brain oscillations are a hallmark of Parkinson's disease (PD) pathophysiology and may be related to motor and non-motor symptoms. Mild cognitive impairment (MCI) affects many people with PD even at the time of diagnosis and conversion risks to PD dementia (PDD) are very high. Unfortunately, pharmacotherapies are not addressing cognitive symptoms in PD. Profiling PD cognitive phenotypes (eg. MCI, PDD...) may therefore help inform future treatments. Neurophysiological methods, such as magnetoencephalography (MEG), offer the advantage of observing oscillatory patterns, whose regional and temporal profiles may elucidate how cognitive changes relate to neural mechanisms. We conducted a resting state MEG cross-sectional study of 89 persons with PD stratified into three phenotypic groups: normal cognition, MCI and PDD, in order to identify brain regions and frequencies most associated with each cognitive profile. In addition, a neuropsychological battery was administered to assess each domain of cognition. Our data showed higher power in lower frequency bands (delta and theta) observed along with more severe cognitive impairment, and associated with memory, language, attention and global cognition. Of the total 119 brain parcels assessed during source analysis, widespread group differences were found in the beta band, with significant changes mostly occurring between the normal cognition and MCI groups. Moreover, bilateral frontal and left-hemispheric regions were particularly affected in the other frequencies as cognitive decline becomes more pronounced. Our results suggest MCI and PDD may be qualitatively distinct cognitive phenotypes, and most dramatic changes seem to have happened when the PD brain shows mild cognitive decline.

Daily Physical Activity Patterns: A Window on Cognitive Decline in the Baltimore Longitudinal Study of Aging (BLSA)

Gradual disengagement from essential daily physical activity (PA) necessary for independent living could signal present or emerging mild cognitive impairment (MCI) or Alzheimer’s disease (AD). We used BLSA data to examine whether PA patterns including: 1) total activity counts/day, 2) minutes/day spent active, and 3) activity fragmentation (reciprocal of the mean active bout length) differs between participants with adjudicated normal cognition (n=498) and MCI/AD diagnoses (n=32). Linear models were used and adjusted for demographics, APOE-e4 status, morbidity, and gait speed. Compared to those with normal cognition, those with MCI/AD had 3.0% higher activity fragmentation (SE=1.1%, p=0.006) but similar mean total activity counts/day (p=0.08) and minutes/day spent active (p=0.19). Results suggest that activity fragmentation may arise as a compensatory strategy in the absence of reduced activity in MCI and early AD and that activity monitoring may be potentially useful for detecting MCI and AD at an earlier stage.

Does Cognitive Status Moderate the Relationship between Environmental Factors and Self-Reported Health?

Using data from NHATS Round 9, the present study examines the relationships between environmental factors and self-reported health among older adults with dementia, mild cognitive impairment (MCI), and normal cognition. Based on neighborhood stress process theory, we investigate the following questions: 1) Are there associations between dwelling safety hazards and neighborhood environments and self-reported health? 2) Is cognitive status a moderator between the relationship? 3) How do these associations differ between older adults with varying cognitive status (i.e., dementia, MCI, and normal cognition)? A hierarchical linear regression analyses are conducted. Results indicate that better quality of sidewalk surface and neighborhood social cohesion are associated with better self-reported health, after taking into account sociodemographic, health, and social factors. Interaction terms are then used to examine the moderating effects of cognitive status on the associations; four interactions terms are found to be statistically significant. Lastly, separate linear regression analyses are implemented for the dementia, MCI, and normal cognition groups. Findings show that the predicting power of environmental factors vary by cognitive status of older adults. For individuals with dementia, tripping hazards, cluttered home, and community disconnectedness are associated with poor self-reported health. However, no significant relationship was found for older adults with MCI. For older adults with normal cognition, better quality of sidewalk surface and neighborhood social cohesion predict better self-rated health scores. Findings of this study illuminate the important role of a hazard-free home, community walkability, and socially cohesive neighborhood environments in predicting better health status of older adults.