scholarly journals Individual variation in brain microstructural-cognition relationships in aging

2021 ◽  
Author(s):  
Raihaan Patel ◽  
Clare E. Mackay ◽  
Michelle G. Jansen ◽  
Gabriel A. Devenyi ◽  
M. Clare O’Donoghue ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Cognitive Performance ◽  
Late Life ◽  
Older Age ◽  
Data Driven ◽  
Semantic Fluency ◽  
Cognitive Domains ◽  
Age Related ◽  
Brain Microstructure ◽  
Age Related Cognitive Decline

AbstractWhile all individuals are susceptible to age-related cognitive decline, significant inter- and intra-individual variability exists. However, the sources of this variation remain poorly understood. Here, we examined the association between 30-year trajectories of cognitive decline and multimodal indices of brain microstructure and morphology in older age. We used the Whitehall II Study, an extensively characterised cohort using 3T brain magnetic resonance images acquired at older age (mean age = 69.52 ± 4.9) and 5 repeated cognitive performance assessments between mid-life (mean age = 53.2 ± 4.9 years) and late-life (mean age = 67.7 ± 4.9). Using non-negative matrix factorization, we identified 10 brain microstructural components that integrate measures of cortical thickness, surface area, fractional anisotropy, and mean and radial diffusivities. We observed two modes of variance that describe the association between cognition and brain microstructure. The first describes variations in 5 microstructural components associated with low mid-life performance across multiple cognitive domains, decline in reasoning abilities, but a relative maintenance of lexical and semantic fluency from mid-to-late life. The second describes variations in 5 microstructural components that are associated with low mid-life performance in lexical fluency, semantic fluency and short-term memory performance, but a retention of abilities in multiple domains from mid-to-late life. The extent to which a subject loads onto a latent variables predicts their future cognitive performance 3.2 years later (mean age = 70.87 ± 4.9). This data-driven approach highlights a complex pattern of brain-behavior relationships, wherein the same individuals express both decline and maintenance in function across cognitive domains and in brain structural features.Significance StatementAlthough declines in cognitive performance are an established aspect of aging, inter- and intra-individual variation exists. Nevertheless, the sources of this variation remain unclear. We analyse a unique sample to examine associations between 30-year trajectories of cognitive decline and multimodal indices of brain anatomy in older age. Using data-driven techniques, we find that age-related cognitive decline is not uniform. Instead, each individual expresses a mixture of maintenance and decline across cognitive domains, that are associated with a mixture of preservation and degeneration of brain structure. Further, we find the primary determinants of late-life cognitive performance are mid-life performance and higher brain surface area. These results suggest that early and mid-life preventative measures may be needed to reduce age-related cognitive decline.

scholarly journals Multimodal measurement approach to identify individuals with mild cognitive impairment: study protocol for a cross-sectional trial

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e046879
Author(s):  
Bernhard Grässler ◽  
Fabian Herold ◽  
Milos Dordevic ◽  
Tariq Ali Gujar ◽  
Sabine Darius ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Statistical Analysis ◽  
Cognitive Decline ◽  
Cognitive Performance ◽  
Classification Accuracy ◽  
Multimodal Approach ◽  
Cross Sectional ◽  
Age Related ◽  
Age Related Cognitive Decline

IntroductionThe diagnosis of mild cognitive impairment (MCI), that is, the transitory phase between normal age-related cognitive decline and dementia, remains a challenging task. It was observed that a multimodal approach (simultaneous analysis of several complementary modalities) can improve the classification accuracy. We will combine three noninvasive measurement modalities: functional near-infrared spectroscopy (fNIRS), electroencephalography and heart rate variability via ECG. Our aim is to explore neurophysiological correlates of cognitive performance and whether our multimodal approach can aid in early identification of individuals with MCI.Methods and analysisThis study will be a cross-sectional with patients with MCI and healthy controls (HC). The neurophysiological signals will be measured during rest and while performing cognitive tasks: (1) Stroop, (2) N-back and (3) verbal fluency test (VFT). Main aims of statistical analysis are to (1) determine the differences in neurophysiological responses of HC and MCI, (2) investigate relationships between measures of cognitive performance and neurophysiological responses and (3) investigate whether the classification accuracy can be improved by using our multimodal approach. To meet these targets, statistical analysis will include machine learning approaches.This is, to the best of our knowledge, the first study that applies simultaneously these three modalities in MCI and HC. We hypothesise that the multimodal approach improves the classification accuracy between HC and MCI as compared with a unimodal approach. If our hypothesis is verified, this study paves the way for additional research on multimodal approaches for dementia research and fosters the exploration of new biomarkers for an early detection of nonphysiological age-related cognitive decline.Ethics and disseminationEthics approval was obtained from the local Ethics Committee (reference: 83/19). Data will be shared with the scientific community no more than 1 year following completion of study and data assembly.Trial registration numberClinicalTrials.gov, NCT04427436, registered on 10 June 2020, https://clinicaltrials.gov/ct2/show/study/NCT04427436.

scholarly journals Subtypes of Late-Life Depression: A Data-Driven Approach on Cognitive Domains and Physical Frailty

2020 ◽  
Vol 76 (1) ◽  
pp. 141-150 ◽  
Author(s):  
Astrid Lugtenburg ◽  
Marij Zuidersma ◽  
Klaas J Wardenaar ◽  
Ivan Aprahamian ◽  
Didi Rhebergen ◽  
...  
Keyword(s):  
Depressive Symptom ◽  
Cognitive Performance ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Late Life ◽  
Late Life Depression ◽  
Physical Frailty ◽  
Cognitive Domains ◽  
Age Related ◽  
Latent Profile

Abstract Background With increasing age, symptoms of depression may increasingly overlap with age-related physical frailty and cognitive decline. We aim to identify late-life-related subtypes of depression based on measures of depressive symptom dimensions, cognitive performance, and physical frailty. Methods A clinical cohort study of 375 depressed older patients with a DSM-IV depressive disorder (acronym NESDO). A latent profile analysis was applied on the three subscales of the Inventory of Depressive Symptomatology, as well as performance in five cognitive domains and two proxies for physical frailty. For each class, we investigated remission, dropout, and mortality at 2-year follow-up as well as change over time of depressive symptom severity, cognitive performance, and physical frailty. Results A latent profile analysis model with five classes best described the data, yielding two subgroups suffering from pure depression (“mild” and “severe” depression, 55% of all patients) and three subgroups characterized by a specific profile of cognitive and physical frailty features, labeled as “amnestic depression,” “frail-depressed, physically dominated,” and “frail-depressed, cognitively dominated.” The prospective analyses showed that patients in the subgroup of “mild depression” and “amnestic depression” had the highest remission rates, whereas patients in both frail-depressed subgroups had the highest mortality rates. Conclusions Late-life depression can be subtyped by specific combinations of age-related clinical features, which seems to have prospective relevance. Subtyping according to the cognitive profile and physical frailty may be relevant for studies examining underlying disease processes as well as to stratify treatment studies on the effectiveness of antidepressants, psychotherapy, and augmentation with geriatric rehabilitation.

Do Professors Better Maintain Cognitive Functioning in Older Age?

GeroPsych ◽  
2019 ◽  
Vol 32 (1) ◽  
pp. 5-17
Author(s):  
Damaris Aschwanden ◽  
Vera Schumacher ◽  
Kathrin Zimmermann ◽  
Christina Werner ◽  
Mathias Allemand ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Cognitive Performance ◽  
Cognitive Aging ◽  
Protective Factor ◽  
Perceptual Speed ◽  
Highly Educated ◽  
Intellectual Engagement ◽  
Age Related ◽  
Average Education ◽  
Age Related Cognitive Decline

Abstract. Research on cognitive aging demonstrates age-related cognitive decline. Education is a protective factor against cognitive decline, but few studies have examined the cognitive development of highly educated individuals. This study compared the cognitive performance and intellectual engagement of retired professors ( N = 47, Mage = 72.9) and individuals with average education ( N = 236, Mage = 72.7) over 5 years. Although the highly educated sample showed better performance in perceptual speed and working memory, cognitive performance was rather stable over time in both samples. Interestingly, high intellectual engagement enabled individuals with average education to keep up with the performance of the highly educated sample on perceptual speed. These findings raise the question whether intellectual engagement is more beneficial than years of education in perceptual speed.

P1-118: The Tasmanian Healthy Brain Study (THBS): Does late-life education prevent age-related cognitive decline and dementia?

2012 ◽  
Vol 8 (4S_Part_4) ◽  
pp. P147-P147
Author(s):  
Mathew Summers ◽  
Michael Valenzuela ◽  
Jeffery Summers ◽  
Karen Ritchie ◽  
Tracey Dickson ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Late Life ◽  
Age Related ◽  
Life Education ◽  
Age Related Cognitive Decline ◽  
Brain Study

Epidemiology of Cerebrovascular Disease Related Cognitive Decline

2003 ◽  
Vol 15 (S1) ◽  
pp. 105-110
Author(s):  
Chengxuan Qiu ◽  
Laura Fratiglioni
Keyword(s):  
Cerebrovascular Disease ◽  
Cognitive Decline ◽  
Memory Function ◽  
Population Based ◽  
Central Component ◽  
Cognitive Domains ◽  
Treatment And Prevention ◽  
Dementia Patients ◽  
Age Related ◽  
Age Related Cognitive Decline

Cognitive decline is a central component of the dementia process. Population-based prospective studies have confirmed the existence of age-related cognitive decline, although its conceptual basis and nosological status remain controversial. Healthy old people show decline with aging in global cognition and memory function in particular. Preclinical and clinical dementia patients exhibit deficits across multiple cognitive domains, with the largest and most consistent deficits in memory function. Cerebrovascluar disease may lead to cognitive decline and promote the clinical expression of dementia directly or by interaction with APOE η4. Early treatment and prevention of cerebrovascular disease may be the major measures for preventing and postponing the progression of the vascular disease related cognitive decline.

Gamma-Glutamyltransferase (GGT) as a biomarker of cognitive decline at the end of life: contrasting age and time to death trajectories

2017 ◽  
Vol 30 (7) ◽  
pp. 981-990 ◽  
Author(s):  
Marcus Praetorius Björk ◽  
Boo Johansson
Keyword(s):  
Longitudinal Study ◽  
Cognitive Decline ◽  
Cognitive Performance ◽  
Late Life ◽  
Cognitive Change ◽  
Gamma Glutamyltransferase ◽  
Time To Death ◽  
Age Related ◽  
Increased Risk ◽  
Underlying Mechanisms

ABSTRACTBackground:A recently published study suggests that Gamma-Glutamyltransferase (GGT) in midlife is related to an increased risk of dementia. In the present longitudinal study, we explore the effects of serum GGT on cognitive decline and dementia also in more advanced ages.Methods:We analyzed GGT in a sample of 452 individuals, aged 80 years and older at baseline, with the purpose to explore subsequent effects on cognitive performance. We specifically modeled GGT to cognitive change, time to death, and dementia.Results:Our main finding is that a higher level of GGT is associated with cognitive decline prior to death and vascular dementia in late life. These findings were evident across cognitive domains.Conclusions:This is the first longitudinal study to report on significant associations in late life between GGT, cognitive performance and dementia. Further research is needed to examine the underlying mechanisms of GGT as a marker of age-related cognitive decline.

VStore: A Novel Virtual Reality Assessment of Functional Cognition (Preprint)

2021 ◽  
Author(s):  
Lilla Alexandra Porffy ◽  
Mitul A. Mehta ◽  
Joel Patchitt ◽  
Celia Boussebaa ◽  
Jack Brett ◽  
...  
Keyword(s):  
Working Memory ◽  
Virtual Reality ◽  
Cognitive Decline ◽  
Paired Associate ◽  
Model Fitting ◽  
Paired Associate Learning ◽  
Associate Learning ◽  
Cognitive Domains ◽  
Age Related ◽  
Age Related Cognitive Decline

BACKGROUND Cognitive deficits are present in a number of neuropsychiatric disorders including, Alzheimer’s disease, schizophrenia and depression. Assessments used to measure cognition in these disorders are time-consuming, burdensome, and have low ecological validity. To address these limitations, we developed a novel virtual reality shopping task – VStore. OBJECTIVE This study aims to establish the concurrent and construct validity of VStore in relation to the established computerized cognitive battery, Cogstate; and tests its sensitivity to age related cognitive decline. METHODS Hundred and four healthy volunteers aged 20-79 completed both assessments. Main VStore outcomes included: 1) verbal recall of 12 grocery items, 2) time to collect items, 3) time to select items on a self-checkout machine, 4) time to make the payment, 5) time to order coffee, and 6) total completion time. To establish concurrent validity, bivariate correlations were performed between VStore outcomes and Cogstate tasks measuring attention, processing speed, verbal and visual learning, working memory, executive function, and paired associate learning. Construct validity analysis was also performed to examine which cognitive domains best predicted VStore performance. Finally, two ridge regression models were built using VStore outcomes in the first, and Cogstate outcomes in the second model as predictors of biological age to compare their sensitivity to age-related cognitive decline. RESULTS We found moderate correlations between VStore and Cogstate outcomes. VStore Total Time was best explained by tasks measuring working memory and paired associate learning, in addition to age and technological familiarity, accounting for 46% of the variance. Finally, with λ = 5.16, the model fitting selected five parameters for VStore when predicting biological age (MSE = 185.8, SE= 19.34). With λ = 9.49 for Cogstate, the model fitting selected all eight tasks (MSE = 226.8, SE = 23.48). CONCLUSIONS Our findings suggest that VStore is a promising assessment that engages standard cognitive domains and is sensitive to age-related cognitive decline. CLINICALTRIAL NA

Cognitive change before old age (11 to 70) predicts cognitive change after old age (70 to 82)

2021 ◽  
Author(s):  
Federica Conte ◽  
Judith Okely ◽  
Olivia Hamilton ◽  
Janie Corley ◽  
Danielle Page ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Old Age ◽  
Social Impact ◽  
Adult Life ◽  
Cognitive Change ◽  
Older Age ◽  
Growth Curve Models ◽  
Age Related ◽  
Lothian Birth Cohort ◽  
Age Related Cognitive Decline

Identifying predictors of cognitive decline within older age helps to understand its mechanisms and to identify those at greater risk. Here we examine how cognitive change from 11 to 70 years is associated with cognitive change within older age (70 to 82 years) in the Lothian Birth Cohort 1936 longitudinal study (N=1091 at recruitment). Using latent growth curve models, we estimate rates of change from age 70 to 82 in general cognitive ability (g) and in three cognitive domains: visuospatial, memory and processing speed. g accounted for 71.3% of interindividual change variance. Greater 11-70 cognitive gain predicted slower decline in g over 12 subsequent years (β = .163, p = .001), independently of cognitive level at age 70, and domain-specific change beyond g. These results contribute toward identifying people at higher risk of age-related cognitive decline. Age-related cognitive decline is a significant threat to the quality of life in older age. Its economic and social impact on society will increase together with the steadily rising life expectancy. How can we preserve cognitive health in older age? Researchers have made significant advances in identifying protective and risk factors. However, most studies focus on a limited age range, and cognitive change mechanisms are not yet completely understood. This work takes advantage of almost life-spanning longitudinal data to test if cognitive trajectory across childhood and adulthood can predict cognitive trajectories in older age. Our findings show that earlier change is associated with later change. Some factors related to individual differences in cognitive change might thus operate over much of the adult life course, and certainly before older age. This knowledge can help identify individuals at higher risk of decline and understand the mechanisms and factors responsible.

scholarly journals Cerebral aging: neuropsychological, neuroradiological, and neurometabolic correlates

2001 ◽  
Vol 3 (3) ◽  
pp. 217-228
Keyword(s):  
Cognitive Decline ◽  
Cognitive Changes ◽  
Statistical Manual ◽  
Cognitive Domains ◽  
Diagnostic And Statistical Manual ◽  
Visuospatial Abilities ◽  
Age Related ◽  
Brain Metabolic Activity ◽  
Declarative Learning ◽  
Age Related Cognitive Decline

The aging process is associated with a progressive cognitive decline, but both the extent of this decline and the profile of age-related cognitive changes remain to be clearly established. Currently, cognitive deficits associated with aging may be diagnosed under the categories of age-associated memory impairment, age-associated cognitive impairment, or the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) category of age-related cognitive decline. Age-related decline has been reported for several cognitive domains, such as language (eg, verb naming, verbal fluency), visuospatial abilities (eg, facial discrimination), executive functions (eg, set shifting, problem solving), and memory functions (eg, declarative learning, source memory). There is an age-related decline in brain cortical volume, which primarily involves association cortices and limbic regions. Studies of brain metabolic activity demonstrate an age-related decline in neocortical areas. Activation studies using cognitive tasks demonstrate that older healthy individuals have a different pattern of activation from younger subjects, suggesting thai older subjects may recruit additional brain areas in order to maintain performance.

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