Alzheimer's disease (AD) has a long preclinical stage characterised by the accumulation of brain pathology, which is estimated to begin several decades before the onset of symptoms. A significant ...proportion of older adults harbour such pathology, although many of them may not develop dementia during their lifetimes. Growing evidence suggests that subtle cognitive decline occurs during this preclinical period, but many unanswered questions remain about the nature and timing of changes in different cognitive domains, and associations with life-course predictors. This thesis is based on data from Insight 46, a neuroimaging sub-study of the MRC National Survey of Health and Development (the British 1946 birth cohort). In this population-based sample of 502 adults aged ~70 years, cognitive performance was assessed using standard and novel tests, and associations were investigated between cognition, life-course predictors, genetic risk factors for AD and brain pathologies, with a particular focus on β-amyloid. The key finding was that participants with elevated levels of β-amyloid showed poorer performance across a range of cognitive domains - some of which have received little attention in previous studies - including non-verbal reasoning, intra-individual variability in reaction time, visuomotor integration and memory. Other important results include: independent effects of childhood cognitive ability, educational attainment and adult socioeconomic position on later-life cognition; an association between white matter pathology and slower processing speed; associations between larger whole brain volume and faster performance on several diverse timed measures; and evidence that APOE-ε4 carriers may be advantaged on tests of short-term memory after accounting for the detrimental effect of β-amyloid. These results have implications for the interpretation of cognitive data measured in later life, and for the use of cognitive assessments to detect and track subtle cognitive decline in clinical trials that seek to delay or prevent the onset of AD dementia.
Objective
The BRAIN Study was established to assess the associations between self‐reported concussions and cognitive function among retired rugby players.
Methods
Former elite‐level male rugby union ...players (50+ years) in England were recruited. Exposure to rugby‐related concussion was collected using the BRAIN‐Q tool. The primary outcome measure was the Preclinical Alzheimer Cognitive Composite (PACC). Linear regressions were conducted for the association between concussion and PACC score, adjusting for confounders.
Results
A total of 146 participants were recruited. The mean (standard deviation) length of playing career was 15.8 (5.4) years. A total of 79.5% reported rugby‐related concussion(s). No association was found between concussion and PACC (β –0.03 95% confidence interval (CI): –1.31, 0.26). However, participants aged 80+ years reporting 3+ concussions had worse cognitive function than those without concussion (β –1.04 95% CI: –1.62, –0.47).
Conclusions
Overall there was no association between concussion and cognitive function; however, a significant interaction with age revealed an association in older participants.
Hearing impairment may be a modifiable risk factor for dementia. However, it is unclear how hearing associates with pathologies relevant to dementia in preclinical populations.
Data from 368 ...cognitively healthy individuals born during 1 week in 1946 (age range 69.2-71.9 years), who underwent structural MRI,
F-florbetapir positron emission tomography, pure tone audiometry and cognitive testing as part of a neuroscience substudy the MRC National Survey of Health and Development were analysed. The aim of the analysis was to investigate whether pure tone audiometry performance predicted a range of cognitive and imaging outcomes relevant to dementia in older adults.
There was some evidence that poorer pure tone audiometry performance was associated with lower primary auditory cortex thickness, but no evidence that it predicted in vivo β-amyloid deposition, white matter hyperintensity volume, hippocampal volume or Alzheimer's disease-pattern cortical thickness. A negative association between pure tone audiometry and mini-mental state examination score was observed, but this was no longer evident after excluding a test item assessing repetition of a single phrase.
Pure tone audiometry performance did not predict concurrent β-amyloid deposition, small vessel disease or Alzheimer's disease-pattern neurodegeneration, and had limited impact on cognitive function, in healthy adults aged approximately 70 years.
•It is unclear whether hyperglycemia in adulthood impacts brain outcomes.•HbA1c was associated with lower global brain volumes in females but not in males.•No evidence linking hyperglycemia with ...amyloidosis or cognitive impairments.•Our findings show target organ damage in female brains with hyperglycemia.
Longitudinal studies of the relationship between hyperglycemia and brain health are rare and there is limited information on sex differences in associations. We investigated whether glycosylated hemoglobin (HbA1c) measured at ages of 53, 60–64 and 69 years, and cumulative glycemic index (CGI), a measure of cumulative glycemic burden, were associated with metrics of brain health in later life.
Participants were from Insight 46, a substudy of the Medical Research Council National Survey of Health and Development (NSHD) who undertook volumetric MRI, florbetapir amyloid-PET imaging and cognitive assessments at ages of 69–71. Analyses were performed using linear and logistic regression as appropriate, with adjustment for potential confounders. We observed a sex interaction between HbA1c and whole brain volume (WBV) at all 3 time points. Following stratification of our sample, we observed that HbA1c at all ages, and CGI were positively associated with lower WBV exclusively in females. HbA1c (or CGI) was not associated with amyloid status, white matter hyperintensities (WMHs), hippocampal volumes (HV) or cognitive outcomes in either sex.
Higher HbA1c in adulthood is associated with smaller WBV at 69–71 years in females but not in males. This suggests that there may be preferential target organ damage in the brain for females with hyperglycemia.
Background
We assess if, and at which ages during 30 years of adulthood, undertaking leisure time physical activity (LTPA) is associated with brain health at age 70, and to what extent brain health ...metrics explain the positive association between LTPA and later‐life cognition.
Method
Participants from the British 1946 birth cohort prospectively reported LTPA five times between ages 36 and 69. Metrics were categorised into: not active (no participation/month); active (participated once or more/month); and summed. Participants underwent 18F‐florbetapir Aβ‐PET and MRI at age 70 (n = 468, 49% female). Regression analyses examined associations between LTPA metrics and later‐life brain health including Aβ‐PET, TIV‐adjusted brain, hippocampal and log‐transformed‐white matter hyperintensity (WMH) volume, adjusting for sex, scan age, childhood cognition, education, and childhood socioeconomic position. Effect modification by sex and APOE‐ε4 were examined. The relationship between cumulative LPTA and later‐life cognition (Addenbrooke’s cognitive Examination (ACE‐III)) was assessed adjusting for brain health measures.
Result
Participation in LTPA was associated with better brain health at age 70. For brain volume and WMH volume, the strongest associations were with LTPA at age 69 (Figure 1). Being active in one or more period across adulthood was linked to larger hippocampal volume (Figure 1); this relationship was modified by APOE‐ε4 (p<0.01), with a stronger effect shown in ε4 carriers (Figure 2). LTPA at age 43 was also associated with larger hippocampal volumes. There was no evidence of associations with amyloid status. The positive association between cumulative LTPA and better cognition was not attenuated by any of the brain health measures (Figure 3).
Conclusion
We provide evidence that LTPA across adulthood is linked to brain health at age 70; being active throughout adulthood was associated with larger hippocampal volume, particularly in APOE ε4 carriers; and being active in later‐life was linked to less WMH and larger brain volume at age 70. However, these brain health metrics did not explain the relationship between LPTA and better cognitive scores, suggesting that these pathways may not underlie the inferred cognitive benefit at this age. Our findings warrant further research to shed light on the mechanisms of physical activity as a potential disease‐modifying intervention of brain health.
Abstract
Background
Accelerated Long‐term Forgetting (ALF) is the phenomenon whereby material is retained normally over short intervals (minutes or hours) but forgotten abnormally rapidly over longer ...periods (days or weeks). ALF may be an early marker of cognitive decline, but little is known about its relationships with preclinical Alzheimer’s disease pathology in older adults, and how memory selectivity may influence which material is forgotten.
Method
Participants in ‘Insight 46’, a sub‐study of the MRC National Survey of Health and Development (British 1946 birth cohort), completed cognitive and neuroimaging assessments at two time‐points (baseline at age ∼70; follow‐up ∼2.4 years later). At follow‐up, we assessed Complex Figure Drawing (copy; immediate recall; 30‐minute recall; 7‐day recall). Complex Figure items were categorized as ‘outline’ or ‘detail’ (
Fig1
), to test the hypothesis that forgetting the outline of the structure would be more sensitive to the effect of brain pathologies.
ALF scores were calculated as the proportion of material retained after 7 days, relative to 30 minutes. Rates of cerebral atrophy between baseline and follow‐up were quantified from T1‐weighted MRI using the Brain Boundary Shift Integral (BBSI). β‐amyloid status (positive/negative) was determined from 18F‐Florbetapir‐PET. Baseline serum neurofilament light (NfL) was quantified (Quanterix Simoa assay).
Multivariable regression models were used to investigate the effects (mutually adjusted) of β‐amyloid status, BBSI and NfL on ALF in n = 316 clinically‐normal individuals (50% female; 22% β‐amyloid positive; 30% APOE‐ε4 carriers), and to explore interactions between these predictors, adjusting for potential confounders including prospectively‐collected childhood cognitive ability and education.
Result
‘Outline’ items were better retained than ‘detail’ (
Fig1
). β‐amyloid‐positive participants had poorer ALF scores for ‘outline’ (but not ‘detail’) items (
Fig1C; Table 1
). Unexpectedly, higher NfL was associated with scores for ‘outline’ items (
Table 1
). Greater rate of cerebral atrophy predicted poorer retention among participants with elevated β‐amyloid and higher NfL (
Table 1; Fig2
).
Conclusion
These results provide evidence of associations between biomarkers of brain pathologies and ALF in 73‐year‐olds. Interactions between different biomarkers merit further exploration. ALF may be a sensitive outcome measure for therapeutic trials in preclinical AD. Better retention of ‘outline’ (vs. ‘detail’) items illustrates the strategic role of memory selectivity.
Abstract
Background
Eye‐tracking technology is an innovative tool that holds promise for enhanced dementia screening, offering the potential of brief and quantitative assessment of cognitive ...functions. Critically, instruction‐less eye‐tracking tests may ameliorate some of the issues with complex test instructions and linguistic variations associated with traditional cognitive tests, and capture additional sensitive metrics of task performance. However, the extraction of relevant biomarkers from large, complex eye‐tracking datasets is non‐trivial. In this work, we introduce a novel automated way of extracting abnormal oculomotor biomarkers using machine learning from raw eye‐tracking data acquired during an instruction‐less cognitive test.
Method
A free‐viewing instruction‐less cognitive battery (5 minutes) was administered to healthy controls (N=553) and patients with a range of dementias (N = 30) Figure 1. Our method is based on self‐supervised representation learning: a deep neural network is initially trained to solve a pretext task that has well‐defined available labels. Here the pretext task is to identify distinct tasks ‐ scene perception, reading, episodic memory for scenes ‐ in healthy individuals from eye‐tracking patterns. Figure 2 visualises some features of eye‐tracking patterns that correspond to particular tasks. Once trained, this network encodes high‐level semantic information which is useful for solving other problems of interests (e.g. dementia classification) Figure 3. The extent to which eye‐tracking features of patients with dementia deviate from healthy behaviour is then explored, followed by a comparison between self‐supervised and handcrafted representations on discriminating between controls and patients.
Result
Based on the results of the handcrafted features, patients with dementia had significantly lower scanpath lengths than controls (z = ‐276.56, SE = 97.09, p =0.00439), consistent with less extensive and efficient scanning of the presented stimuli. The self‐supervised learning features showed higher performance in discriminating dementia patients from controls (F1 score (95% CI: 0.78, 0.82) vs standard handcrafted features 0.62, 0.67).
Conclusion
These results suggest that instruction‐less eye‐tracking tests can detect dementia status, even in the absence of explicit task instructions. We reveal novel self‐supervised learning features that are more sensitive than handcrafted features in detecting performance differences between participants with and without dementia across a variety of eye‐tracking‐based cognitive tasks.
Background
Eye‐tracking technology is an innovative tool that holds promise for enhanced dementia screening, offering the potential of brief and quantitative assessment of cognitive functions. ...Critically, instruction‐less eye‐tracking tests may ameliorate some of the issues with complex test instructions and linguistic variations associated with traditional cognitive tests, and capture additional sensitive metrics of task performance. However, the extraction of relevant biomarkers from large, complex eye‐tracking datasets is non‐trivial. In this work, we introduce a novel automated way of extracting abnormal oculomotor biomarkers using machine learning from raw eye‐tracking data acquired during an instruction‐less cognitive test.
Method
A free‐viewing instruction‐less cognitive battery (5 minutes) was administered to healthy controls (N=553) and patients with a range of dementias (N = 30) Figure 1. Our method is based on self‐supervised representation learning: a deep neural network is initially trained to solve a pretext task that has well‐defined available labels. Here the pretext task is to identify distinct tasks ‐ scene perception, reading, episodic memory for scenes ‐ in healthy individuals from eye‐tracking patterns. Figure 2 visualises some features of eye‐tracking patterns that correspond to particular tasks. Once trained, this network encodes high‐level semantic information which is useful for solving other problems of interests (e.g. dementia classification) Figure 3. The extent to which eye‐tracking features of patients with dementia deviate from healthy behaviour is then explored, followed by a comparison between self‐supervised and handcrafted representations on discriminating between controls and patients.
Result
Based on the results of the handcrafted features, patients with dementia had significantly lower scanpath lengths than controls (z = ‐276.56, SE = 97.09, p =0.00439), consistent with less extensive and efficient scanning of the presented stimuli. The self‐supervised learning features showed higher performance in discriminating dementia patients from controls (F1 score (95% CI: 0.78, 0.82) vs standard handcrafted features 0.62, 0.67).
Conclusion
These results suggest that instruction‐less eye‐tracking tests can detect dementia status, even in the absence of explicit task instructions. We reveal novel self‐supervised learning features that are more sensitive than handcrafted features in detecting performance differences between participants with and without dementia across a variety of eye‐tracking‐based cognitive tasks.
PDF
