Dataset Information

Spatial navigation ability predicts progression of dementia symptomatology.

ABSTRACT:

Introduction

Spatial navigation deficits are observed in Alzheimer's disease cross-sectionally, but prediction of longitudinal clinical decline has been less examined.

Methods

Cognitive mapping (CM) was assessed in 95 participants and route learning (RL) was assessed in 65 participants at baseline. Clinical progression over an average of 4 to 5 years was assessed using the clinical dementia rating (CDR) scale. Relative predictive ability was compared to episodic memory, hippocampus, and cerebrospinal fluid biomarkers (phosphorylated tau/amyloid ? 42 (ptau₁₈₁ /A?₄₂ ) ratio).

Results

CM and RL were predictors of clinical progression (P's < 0.032). All measures, except RL-Learning remained predictors with episodic memory in models (P's < 0.048). Only RL-Retrieval remained a predictor when ptau₁₈₁ /A?₄₂ was included (P < 0.001). CM interacted with hippocampus and ptau₁₈₁ /A?₄₂ in prediction (P's < 0.013). CM, RL, and episodic memory evidenced strong diagnostic accuracy (area under the curve (AUC) = 0.894, 0.794, and 0.735, respectively); CM tended to perform better than episodic memory (P = 0.056).

Discussion

Baseline spatial navigation performance may be appropriate for assessing risk of clinical progression.

SUBMITTER: Levine TF

PROVIDER: S-EPMC7067640 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Spatial navigation ability predicts progression of dementia symptomatology.

Levine Taylor F TF Allison Samantha L SL Stojanovic Marta M Fagan Anne M AM Morris John C JC Head Denise D

Alzheimer's & dementia : the journal of the Alzheimer's Association 20200211 3

<h4>Introduction</h4>Spatial navigation deficits are observed in Alzheimer's disease cross-sectionally, but prediction of longitudinal clinical decline has been less examined.<h4>Methods</h4>Cognitive mapping (CM) was assessed in 95 participants and route learning (RL) was assessed in 65 participants at baseline. Clinical progression over an average of 4 to 5 years was assessed using the clinical dementia rating (CDR) scale. Relative predictive ability was compared to episodic memory, hippocampu ...[more]

PMID: 32043719

Similar Datasets

Project description:As the rate of urbanization continues to increase globally, a growing body of research is emerging that investigates how urbanization shapes the movement-and consequent gene flow-of species in cities. Of particular interest are native species that persist in cities, either as small relict populations or as larger populations of synanthropic species that thrive alongside humans in new urban environments. In this study, we used genomic sequence data (SNPs) and spatially explicit individual-based analyses to directly compare the genetic structure and patterns of gene flow in two small mammals with different dispersal abilities that occupy the same urbanized landscape to evaluate how mobility impacts genetic connectivity. We collected 215 white-footed mice (Peromyscus leucopus) and 380 big brown bats (Eptesicus fuscus) across an urban-to-rural gradient within the Providence, Rhode Island (U.S.A.) metropolitan area (population =1,600,000 people). We found that mice and bats exhibit clear differences in their spatial genetic structure that are consistent with their dispersal abilities, with urbanization having a stronger effect on Peromyscus mice. There were sharp breaks in the genetic structure of mice within the Providence urban core, as well as reduced rates of migration and an increase in inbreeding with more urbanization. In contrast, bats showed very weak genetic structuring across the entire study area, suggesting a near-panmictic gene pool likely due to the ability to disperse by flight. Genetic diversity remained stable for both species across the study region. Mice also exhibited a stronger reduction in gene flow between island and mainland populations than bats. This study represents one of the first to directly compare multiple species within the same urban-to-rural landscape gradient, an important gap to fill for urban ecology and evolution. Moreover, here we document the impacts of dispersal capacity on connectivity for native species that have persisted as the urban landscape matrix expands.

Project description:Abstract Biomarkers that can predict disease progression in individuals with genetic frontotemporal dementia are urgently needed. We aimed to identify whether baseline MRI-based grey and white matter abnormalities are associated with different clinical progression profiles in presymptomatic mutation carriers in the GENetic Frontotemporal dementia Initiative. Three hundred eighty-seven mutation carriers were included (160 GRN, 160 C9orf72, 67 MAPT), together with 240 non-carrier cognitively normal controls. Cortical and subcortical grey matter volumes were generated using automated parcellation methods on volumetric 3T T1-weighted MRI scans, while white matter characteristics were estimated using diffusion tensor imaging. Mutation carriers were divided into two disease stages based on their global CDR®+NACC-FTLD score: presymptomatic (0 or 0.5) and fully symptomatic (1 or greater). The w-scores in each grey matter volumes and white matter diffusion measures were computed to quantify the degree of abnormality compared to controls for each presymptomatic carrier, adjusting for their age, sex, total intracranial volume, and scanner type. Presymptomatic carriers were classified as ‘normal’ or ‘abnormal’ based on whether their grey matter volume and white matter diffusion measure w-scores were above or below the cut point corresponding to the 10th percentile of the controls. We then compared the change in disease severity between baseline and one year later in both the ‘normal’ and ‘abnormal’ groups within each genetic subtype, as measured by the CDR®+NACC-FTLD sum-of-boxes score and revised Cambridge Behavioural Inventory total score. Overall, presymptomatic carriers with normal regional w-scores at baseline did not progress clinically as much as those with abnormal regional w-scores. Having abnormal grey or white matter measures at baseline was associated with a statistically significant increase in the CDR®+NACC-FTLD of up to 4 points in C9orf72 expansion carriers, and 5 points in the GRN group as well as a statistically significant increase in the revised Cambridge Behavioural Inventory of up to 11 points in MAPT, 10 points in GRN, and 8 points in C9orf72 mutation carriers. Baseline regional brain abnormalities on MRI in presymptomatic mutation carriers are associated with different profiles of clinical progression over time. These results may be helpful to inform stratification of participants in future trials. Bocchetta et al. quantified brain anomalies on MRI in a large cohort of C9orf72, MAPT, and GRN mutation carriers. They defined the imaging markers associated with the largest clinical and behavioral changes over one year in presymptomatic carriers, providing important data to inform participants’ stratification in trials. Graphical Abstract Graphical abstract

Project description:Background: Plasma-based biomarkers would be potential biomarkers for early diagnosis of Alzheimer's disease (AD) because they are more available and cost-effective than cerebrospinal fluid (CSF) or neuroimaging. Therefore, we aimed to evaluate whether phosphorylated tau181 (p-tau181) in plasma could be an accurate AD predictor. Methods: Participants from the ADNI database included 185 cognitively unimpaired subjects with negative Aβ (CU-), 66 subjects with pre-clinical AD (CU with positive Aβ), 164 subjects with mild cognitive impairment with negative Aβ (MCI-), 254 subjects with prodromal AD (MCI with positive Aβ), and 98 subjects with dementia. Multiple linear regression models, linear mixed-effects models, and local regression were used to explore cross-sectional and longitudinal associations of plasma p-tau181 with cognition, neuroimaging, or CSF biomarkers adjusted for age, sex, education, and APOE genotype. Besides, Kaplan-Meier and adjusted Cox-regression model were performed to predict the risk of progression to dementia. Receiver operating characteristic analyses were performed to evaluate the predictive value of p-tau181. Results: Plasma p-tau181 level was highest in AD dementia, followed by prodromal AD and pre-clinical AD. In pre-clinical AD, plasma p-tau181 was negatively associated with hippocampal volume (β = -0.031, p-value = 0.017). In prodromal AD, plasma p-tau181 was associated with decreased global cognition, executive function, memory, language, and visuospatial functioning (β range -0.119 to -0.273, p-value < 0.05) and correlated with hippocampal volume (β = -0.028, p-value < 0.005) and white matter hyperintensity volume (WMH) volume (β = 0.02, p-value = 0.01). In AD dementia, increased plasma p-tau181 was associated with worse memory. In the whole group, baseline plasma p-tau181 was significantly associated with longitudinal increases in multiple neuropsychological test z-scores and correlated with AD-related CSF biomarkers and hippocampal volume (p-value < 0.05). Meanwhile, CU or MCI with high plasma p-tau181 carried a higher risk of progression to dementia. The area under the curve (AUC) of the adjusted model (age, sex, education, APOE genotype, and plasma p-tau181) was 0.78; that of additionally included CSF biomarkers was 0.84. Conclusions: Plasma p-tau181 level is related to multiple AD-associated cognitive domains and AD-related CSF biomarkers at the clinical stages of AD. Moreover, plasma p-tau181 level is related to the change rates of cognitive decline and hippocampal atrophy. Thus, this study confirms the utility of plasma p-tau181 as a non-invasive biomarker for early detection and prediction of AD.

Dataset Information

Spatial navigation ability predicts progression of dementia symptomatology.

Introduction

Methods

Results

Discussion

Publications

Spatial navigation ability predicts progression of dementia symptomatology.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets