scholarly journals Grey matter network trajectories across the Alzheimer’s disease continuum and relation to cognition

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Ellen Dicks ◽  
Lisa Vermunt ◽  
Wiesje M van der Flier ◽  
Frederik Barkhof ◽  
Philip Scheltens ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Executive Functioning ◽  
Cognitive Decline ◽  
Grey Matter ◽  
Disease Stage ◽  
Network Measures ◽  
Cognitive Continuum ◽  
Alzheimer’S Disease Dementia ◽  
Over Time

Abstract Biomarkers are needed to monitor disease progression in Alzheimer’s disease. Grey matter network measures have such potential, as they are related to amyloid aggregation in cognitively unimpaired individuals and to future cognitive decline in predementia Alzheimer’s disease. Here, we investigated how grey matter network measures evolve over time within individuals across the entire Alzheimer’s disease cognitive continuum and whether such changes relate to concurrent decline in cognition. We included 190 cognitively unimpaired, amyloid normal (controls) and 523 individuals with abnormal amyloid across the cognitive continuum (preclinical, prodromal, Alzheimer’s disease dementia) from the Alzheimer’s Disease Neuroimaging Initiative and calculated single-subject grey matter network measures (median of five networks per individual over 2 years). We fitted linear mixed models to investigate how network measures changed over time and whether such changes were associated with concurrent changes in memory, language, attention/executive functioning and on the Mini-Mental State Examination. We further assessed whether associations were modified by baseline disease stage. We found that both cognitive functioning and network measures declined over time, with steeper rates of decline in more advanced disease stages. In all cognitive stages, decline in network measures was associated with concurrent decline on the Mini-Mental State Examination, with stronger effects for individuals closer to Alzheimer’s disease dementia. Decline in network measures was associated with concurrent cognitive decline in different cognitive domains depending on disease stage: In controls, decline in networks was associated with decline in memory and language functioning; preclinical Alzheimer’s disease showed associations of decline in networks with memory and attention/executive functioning; prodromal Alzheimer’s disease showed associations of decline in networks with cognitive decline in all domains; Alzheimer’s disease dementia showed associations of decline in networks with attention/executive functioning. Decline in grey matter network measures over time accelerated for more advanced disease stages and was related to concurrent cognitive decline across the entire Alzheimer’s disease cognitive continuum. These associations were disease stage dependent for the different cognitive domains, which reflected the respective cognitive stage. Our findings therefore suggest that grey matter measures are helpful to track disease progression in Alzheimer’s disease.

Pharmacogenetics of Angiotensin-Converting Enzyme Inhibitors in Patients with Alzheimer's Disease Dementia

2018 ◽  
Vol 15 (4) ◽  
pp. 386-398 ◽  
Author(s):  
Fabricio Ferreira de Oliveira ◽  
Elizabeth Suchi Chen ◽  
Marilia Cardoso Smith ◽  
Paulo Henrique Ferreira Bertolucci
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Angiotensin Converting Enzyme ◽  
Cognitive Decline ◽  
Enzyme Inhibitors ◽  
Late Onset ◽  
Amyloid Β ◽  
Converting Enzyme ◽  
Converting Enzyme Inhibitors ◽  
Alzheimer’S Disease Dementia

Background: While the angiotensin-converting enzyme degrades amyloid-β, angiotensinconverting enzyme inhibitors (ACEis) may slow cognitive decline by way of cholinergic effects, by increasing brain substance P and boosting the activity of neprilysin, and by modulating glucose homeostasis and augmenting the secretion of adipokines to enhance insulin sensitivity in patients with Alzheimer’s disease dementia (AD). We aimed to investigate whether ACE gene polymorphisms rs1800764 and rs4291 are associated with cognitive and functional change in patients with AD, while also taking APOE haplotypes and anti-hypertensive treatment with ACEis into account for stratification. Methods: Consecutive late-onset AD patients were screened with cognitive tests, while their caregivers were queried for functional and caregiver burden scores. Prospective pharmacogenetic correlations were estimated for one year, considering APOE and ACE genotypes and haplotypes, and treatment with ACEis. Results: For 193 patients, minor allele frequencies were 0.497 for rs1800764 – C (44.6% heterozygotes) and 0.345 for rs4291 – T (38.9% heterozygotes), both in Hardy-Weinberg equilibrium. Almost 94% of all patients used cholinesterase inhibitors, while 155 (80.3%) had arterial hypertension, and 124 used ACEis. No functional impacts were found regarding any genotypes or pharmacological treatment. Either for carriers of ACE haplotypes that included rs1800764 – T and rs4291 – A, or for APOE4- carriers of rs1800764 – T or rs4291 – T, ACEis slowed cognitive decline independently of blood pressure variations. APOE4+ carriers were not responsive to treatment with ACEis. Conclusion: ACEis may slow cognitive decline for patients with AD, more remarkably for APOE4- carriers of specific ACE genotypes.

Preclinical Alzheimer's disease

2014 ◽  
Vol 24 (2) ◽  
pp. 117-121
Author(s):  
P Gil-Gregorio ◽  
R Yubero-Pancorbo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Diagnostic Criteria ◽  
Stage I ◽  
Disease Stage ◽  
Synaptic Dysfunction ◽  
Preclinical Alzheimer’S Disease ◽  
Cerebral Amyloidosis ◽  
Three Stages

SummaryRecently, diagnostic criteria for preclinical Alzheimer's disease have been proposed. These describe and define three stages of disease. Stage I is focused on asymptomatic cerebral amyloidosis. Stage II includes evidence of synaptic dysfunction and/or early degeneration. Finally, stage III of the disease is characterized by the beginning of cognitive decline.

scholarly journals Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer’s disease

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Lisa Vermunt ◽  
Ellen Dicks ◽  
Guoqiao Wang ◽  
Aylin Dincer ◽  
Shaney Flores ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Symptom Onset ◽  
Grey Matter ◽  
Autosomal Dominant ◽  
Single Subject ◽  
Mutation Carriers ◽  
Autosomal Dominant Alzheimer’S Disease ◽  
Network Properties ◽  
Over Time

Abstract Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer’s disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer’s disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset −9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer’s disease, which is alike sporadic Alzheimer’s disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer’s disease.

scholarly journals The Effect of Traumatic Brain Injury History with Loss of Consciousness on Rate of Cognitive Decline Among Older Adults with Normal Cognition and Alzheimer’s Disease Dementia

2017 ◽  
Vol 59 (1) ◽  
pp. 251-263 ◽  
Author(s):  
Yorghos Tripodis ◽  
Michael L. Alosco ◽  
Nikolaos Zirogiannis ◽  
Brandon E. Gavett ◽  
Christine Chaisson ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Cognitive Decline ◽  
Loss Of Consciousness ◽  
Normal Cognition ◽  
Alzheimer’S Disease Dementia

scholarly journals Neuropsychiatric Symptoms in Mild Cognitive Impairment and Dementia Due to AD: Relation With Disease Stage and Cognitive Deficits

2021 ◽  
Vol 12 ◽  
Author(s):  
Wietse A. Wiels ◽  
Mandy M. J. Wittens ◽  
Dieter Zeeuws ◽  
Chris Baeken ◽  
Sebastiaan Engelborghs
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Depressive Symptoms ◽  
Mild Cognitive Impairment ◽  
Cognitive Decline ◽  
Neuropsychiatric Symptoms ◽  
Disease Stage ◽  
Research Database ◽  
Severe Dementia

Background: The interaction between neuropsychiatric symptoms, mild cognitive impairment (MCI), and dementia is complex and remains to be elucidated. An additive or multiplicative effect of neuropsychiatric symptoms such as apathy or depression on cognitive decline has been suggested. Unraveling these interactions may allow the development of better prevention and treatment strategies. In the absence of available treatments for neurodegeneration, a timely and adequate identification of neuropsychiatric symptom changes in cognitive decline is highly relevant and can help identify treatment targets.Methods: An existing memory clinic-based research database of 476 individuals with MCI and 978 individuals with dementia due to Alzheimer's disease (AD) was reanalyzed. Neuropsychiatric symptoms were assessed in a prospective fashion using a battery of neuropsychiatric assessment scales: Middelheim Frontality Score, Behavioral Pathology in Alzheimer's Disease Rating Scale (Behave-AD), Cohen-Mansfield Agitation Inventory, Cornell Scale for Depression in Dementia (CSDD), and Geriatric Depression Scale (30 items). We subtyped subjects suffering from dementia as mild, moderate, or severe according to their Mini-Mental State Examination (MMSE) score and compared neuropsychiatric scores across these groups. A group of 126 subjects suffering from AD with a significant cerebrovascular component was examined separately as well. We compared the prevalence, nature, and severity of neuropsychiatric symptoms between subgroups of patients with MCI and dementia due to AD in a cross-sectional analysis.Results: Affective and sleep-related symptoms are common in MCI and remain constant in prevalence and severity across dementia groups. Depressive symptoms as assessed by the CSDD further increase in severe dementia. Most other neuropsychiatric symptoms (such as agitation and activity disturbances) progress in parallel with severity of cognitive decline. There are no significant differences in neuropsychiatric symptoms when comparing “pure” AD to AD with a significant vascular component.Conclusion: Neuropsychiatric symptoms such as frontal lobe symptoms, psychosis, agitation, aggression, and activity disturbances increase as dementia progresses. Affective symptoms such as anxiety and depressive symptoms, however, are more frequent in MCI than mild dementia but otherwise remain stable throughout the cognitive spectrum, except for an increase in CSDD score in severe dementia. There is no difference in neuropsychiatric symptoms when comparing mixed dementia (defined here as AD + significant cerebrovascular disease) to pure AD.

scholarly journals Using Digital Speech Assessments to Detect Early Signs of Cognitive Impairment

2021 ◽  
Vol 3 ◽  
Author(s):  
Jessica Robin ◽  
Mengdan Xu ◽  
Liam D. Kaufman ◽  
William Simpson
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Cognitive Decline ◽  
Screening Tools ◽  
Rapid Decline ◽  
Assessment Task ◽  
Early Signs ◽  
Over Time

Detecting early signs of cognitive decline is crucial for early detection and treatment of Alzheimer's Disease. Most of the current screening tools for Alzheimer's Disease represent a significant burden, requiring invasive procedures, or intensive and costly clinical testing. Recent findings have highlighted changes to speech and language patterns that occur in Alzheimer's Disease, and may be detectable prior to diagnosis. Automated tools to assess speech have been developed that can be used on a smartphone or tablet, from one's home, in under 10 min. In this study, we present the results of a study of older adults who completed a digital speech assessment task over a 6-month period. Participants were grouped according to those who scored above (N = 18) or below (N = 18) the recommended threshold for detecting cognitive impairment on the Montreal Cognitive Assessment (MoCA) and those with diagnoses of mild cognitive impairment (MCI) or early Alzheimer's Disease (AD) (N = 14). Older adults who scored above the MoCA threshold had better performance on speech composites reflecting language coherence, information richness, syntactic complexity, and word finding abilities. Those with MCI and AD showed more rapid decline in the coherence of language from baseline to 6-month follow-up, suggesting that this score may be useful both for detecting cognitive decline and monitoring change over time. This study demonstrates that automated speech assessments have potential as sensitive tools to detect early signs of cognitive impairment and monitor progression over time.

scholarly journals 3265 Analysis of High-Dimensional Patient Data in Characterizing Alzheimer’s Disease Progression

2019 ◽  
Vol 3 (s1) ◽  
pp. 3-3
Author(s):  
Daniel Baer ◽  
Andrew B. Lawson ◽  
Brandon Vaughan ◽  
Jane E. Joseph
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Cognitive Decline ◽  
Brain Regions ◽  
Clustering Coefficient ◽  
Eigenvector Centrality ◽  
Research Hypothesis ◽  
The Brain ◽  
Over Time

OBJECTIVES/SPECIFIC AIMS: Our research hypothesis is that resting state fMRI (rsfMRI) data can be used to identify regions of the brain which are associated with cognitive decline in patients – thereby providing a tool by which to characterize AD progression in patients. METHODS/STUDY POPULATION: We used data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to analyze Mini-Mental State Examination (MMSE) questionnaire scores from 14 patients diagnosed with AD at two measurement occasions. RsfMRI data was available at the first of these occasions for these patients. These rsfMRI data were summarized into 264 node-based graph theory measures of clustering coefficient and eigenvector centrality. To address our research hypothesis, we modeled changes in patient MMSE scores over time as a function of these rsfMRI data, controlling for relevant confounding factors. This model accounted for the high-dimensionality of our predictor data, the longitudinal nature of the outcome, and our desire to identify a subset of regions in the brain most associated with the MMSE outcome. RESULTS/ANTICIPATED RESULTS: The use of either the clustering coefficient or eigenvector centrality rsfMRI predictors in modeling MMSE scores for patients over time resulted in the identification of different subsets of brain regions associated with cognitive decline. This suggests that these predictors capture different information on patient propensity for cognitive decline. Further work is warranted to validate these results on a larger sample of ADNI patients. DISCUSSION/SIGNIFICANCE OF IMPACT: We conclude that different rsfMRI graph theory measures capture different aspects of cognitive function and decline in patients, which could be a future consideration in clinical practice.

Longitudinal changes in awareness over 36 months in patients with mild Alzheimer's disease

2014 ◽  
Vol 27 (1) ◽  
pp. 95-102 ◽  
Author(s):  
Asmus Vogel ◽  
Frans Boch Waldorff ◽  
Gunhild Waldemar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Mmse Score ◽  
Neuropsychiatric Symptoms ◽  
Rapid Decline ◽  
Longitudinal Changes ◽  
Moderate Dementia ◽  
Over Time

ABSTRACTBackground:Longitudinal changes in awareness in dementia have been studied with short follow-up time and mostly in small patient groups (including patients with moderate dementia). We investigated awareness in patients with mild Alzheimer's disease (AD) over 36 months and studied if a decline in awareness was associated with decline in cognition and increase in neuropsychiatric symptoms.Methods:Awareness was measured on a categorical scale in 95 AD patients (age ≥50 years, Mini-Mental State Examination (MMSE) score ≥20). Awareness was rated at three time points (follow-up at 12 and 36 months) where MMSE, Neuropsychiatric Inventory (NPI-Q), and Cornell scale for Depression in Dementia also were applied.Results:At 12 months, 26% had lower awareness rating as compared to baseline and at 36 months lower awareness ratings were found in 39%. At both visits, 16% had higher awareness rating as compared to baseline. Patients with lower awareness at 36 months as compared to baseline had a more rapid increase in NPI-Q score (p = 0.002) over 36 months as compared to patients with stable or improved awareness over 36 months. A more rapid decline in MMSE score was observed for patients with lower awareness at 36 months (as compared to baseline) but only when compared to patients in whom awareness improved over time.Conclusions:The results show essentially no clear relationship between cognitive decline over three years and awareness. In some cases, awareness remained stable or even improved despite significant cognitive decline. In the subgroup where awareness declined over time, overall ratings of neuropsychiatric symptoms declined more rapidly than in the remaining patients.

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