Anosognosia predicts default mode network hypometabolism and clinical progression to dementia

Olfactory anosognosia is a predictor of cognitive decline and dementia conversion in Parkinson's disease

OBJECTIVE

Parkinson's disease (PD) patients are often unaware of olfactory deficits despite having hyposmia from the early stages. We aimed to evaluate whether olfactory anosognosia is a predictor of cognitive decline in PD.

METHODS

In this retrospective cohort study, we recruited 77 PD patients who underwent both olfactory and neuropsychological tests and were followed-up for over 5 years. Based on the degree of olfactory dysfunction and awareness of its presence, patients were classified as normosmic patients (Normosmia group, n = 15), hyposmic patients without olfactory anosognosia (Hyposmia-OA-, n = 40), or hyposmic patients with olfactory anosognosia (Hyposmia-OA+, n = 22). We compared the rates of cognitive decline using linear mixed model and dementia conversion using a survival analysis among the groups.

RESULTS

A higher proportion of patients in the Hyposmia-OA+ group had mild cognitive impairment at baseline (77.3%) and dementia converter at follow-up (50.0%). The Hyposmia-OA+ group exhibited a faster decline in frontal executive and global cognitive function than did the Normosmia and Hyposmia-OA- groups. A Kaplan-Meier analysis demonstrated that the conversion rate to dementia was significantly higher in the Hyposmia-OA+ group than in the Normosmia (P = 0.007) and Hyposmia-OA- (P = 0.038) groups. A Cox regression analysis showed that olfactory anosognosia remained a significant predictor of time to develop dementia in the Hyposmia-OA+ group compared to the Normosmia group (adjusted hazard ratio 3.30; 95% confidence interval 1.10-8.21).

CONCLUSION

This study suggests that olfactory anosognosia is a predictor of cognitive decline and dementia conversion in PD.

A Preliminary Study of Clinical Trial Enrollment Decisions Among People With Mild Cognitive Impairment and Their Study Partners

OBJECTIVE

All Alzheimer disease (AD) clinical trials, including those enrolling patients with mild cognitive impairment (MCI), require dyadic participation. The purpose of this study was to elucidate how people with MCI and their study partners decide whether to enroll in clinical trials.

METHODS

This was a mixed methods interview study. We interviewed patient participants with a consensus research diagnosis of MCI and their study partners. Interviews examined how dyads decide whether to enroll in a clinical trial and whether AD biomarker testing affects willingness to enroll.

RESULTS

Though most MCI patients and study partners would decide in partnership whether to enroll in a clinical trial, agreement was lower among nonspousal, compared with spousal, dyads. Deterrents to enrollment included concerns about patient safety and inconvenience, especially for study partners. Motivators to enrollment included altruism, the desire to contribute to research, hope for patient benefit, and the desire to learn more about the patient's condition. When asked open-ended questions about motivators to enroll in trials, few patients cited access to biomarker testing specifically, though most expressed a desire to undergo biomarker testing when asked directly.

CONCLUSION

Spousal and nonspousal MCI dyads may approach clinical trial decisions differently. Future research should investigate how AD biomarker testing affects participants' willingness to enroll in trials.

Cingulate-centered large-scale networks: Normal functions, aging, and neurodegenerative disease

In this chapter, we review evidence from structural and functional neuroimaging in humans to consider the role of the cingulate cortex subregions (i.e., subgenual anterior cingulate cortex, pregenual anterior cingulate cortex, anterior midcingulate cortex, and dorsal posterior cingulate cortex) as major hubs anchoring multiple large-scale brain networks. We begin with a review of evidence from intrinsic functional connectivity and diffusion tensor imaging studies to show how connections within and between cingulate-centered networks contribute to processing and integrating signals related to autonomic, affective, executive, and memory functions. We then consider how variability in cingulate-centered networks could contribute to a range of aging outcomes, including typical aging and unusually successful aging (dubbed "superaging"), as well as early neurodegenerative dementias, including frontotemporal dementia and Alzheimer's disease.

Metastable neural dynamics in Alzheimer's disease are disrupted by lesions to the structural connectome

Current theory suggests brain regions interact to reconcile the competing demands of integration and segregation by leveraging metastable dynamics. An emerging consensus recognises the importance of metastability in healthy neural dynamics where the transition between network states over time is dependent upon the structural connectivity between brain regions. In Alzheimer's disease (AD) - the most common form of dementia - these couplings are progressively weakened, metastability of neural dynamics are reduced and cognitive ability is impaired. Accordingly, we use a joint empirical and computational approach to reveal how behaviourally relevant changes in neural metastability are contingent on the structural integrity of the anatomical connectome. We estimate the metastability of fMRI BOLD signal in subjects from across the AD spectrum and in healthy controls and demonstrate the dissociable effects of structural disconnection on synchrony versus metastability. In addition, we reveal the critical role of metastability in general cognition by demonstrating the link between an individuals cognitive performance and their metastable neural dynamic. Finally, using whole-brain computer modelling, we demonstrate how a healthy neural dynamic is conditioned upon the topological integrity of the structural connectome. Overall, the results of our joint computational and empirical analysis suggest an important causal relationship between metastable neural dynamics, cognition, and the structural efficiency of the anatomical connectome.