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\"Brain aging has long been seen as a process of deterioration and decline. Today, this view been challenged with research showing that not all cognitive processes decline with age, that some improve over the course of adulthood, and those that improve can often compensate for those that decline. Chapters in this multidisciplinary volume examine the neural mechanisms underlying changes in the aging brain, changes in learning and memory, risk and protective factors, and the assessment and prevention of cognitive decline\"--Provided by publisher.

Resting state functional connectivity (rs-fMRI) is impaired early in persons who subsequently develop Alzheimer’s disease (AD) dementia. This impairment may be leveraged to aid investigation of the pre-clinical phase of AD. We developed a model that predicts brain age from resting state (rs)-fMRI data, and assessed whether genetic determinants of AD, as well as beta-amyloid (Aβ) pathology, can accelerate brain aging. Using data from 1340 cognitively unimpaired participants between 18–94 years of age from multiple sites, we showed that topological properties of graphs constructed from rs-fMRI can predict chronological age across the lifespan. Application of our predictive model to the context of pre-clinical AD revealed that the pre-symptomatic phase of autosomal dominant AD includes acceleration of functional brain aging. This association was stronger in individuals having significant Aβ pathology. Alzheimer’s disease has been associated with increased structural brain aging. Here the authors describe a model that predicts brain aging from resting state functional connectivity data, and demonstrate this is accelerated in individuals with pre-clinical familial Alzheimer’s disease.

ObjectiveWe sought to identify the risk factors for predicting the progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD).MethodsWe searched 6 electronic databases for cohort studies published from January 1966 to March 2015. Eligible studies were required to be relevant to the subject and provide sufficient data for our needs.Results60 cohort studies with 14 821 participants from 16 countries were included in the meta-analysis. The strongest positive associations between risk factors and the progression from MCI to AD were found for abnormal cerebrospinal fluid (CSF), phosphorylated τ (p-τ) (relative risk (RR)=2.43, 95% CI=1.70 to 3.48), abnormal CSF τ/Aβ1–42 (RR=3.77, 95% CI=2.34 to 6.09), hippocampal atrophy (RR=2.59, 95% CI=1.95 to 3.44), medial temporal lobe atrophy (RR=2.11, 95% CI=1.70 to 2.63) and entorhinal atrophy (RR=2.03, 95% CI=1.57 to 2.62). Further positive associations were found for the presence of apolipoprotein E (APOE)ε4ε4 and at least 1 APOEε4 allele, CSF total-τ (t-τ), white matter hyperintensity volume, depression, diabetes, hypertension, older age, female gender, lower mini-mental state examination (MMSE) score and higher AD assessment scale cognitive subscale (ADAS-cog) score. Negative associations were found for high body mass index (RR=0.85, 95% CI=0.76 to 0.96) and higher auditory verbal learning test delay score (RR=0.86, 95% CI=0.77 to 0.96).ConclusionsPatients with MCI with APOEε4, abnormal CSF τ level, hippocampal and medial temporal lobe atrophy, entorhinal atrophy, depression, diabetes, hypertension, older age, female gender, lower MMSE score and higher ADAS-cog score, had a high risk for the progression to AD.

With no effective treatments for cognitive decline or dementia, improving the evidence base for modifiable risk factors is a research priority. This study investigated associations between risk factors and late-life cognitive decline on a global scale, including comparisons between ethno-regional groups. We harmonized longitudinal data from 20 population-based cohorts from 15 countries over 5 continents, including 48,522 individuals (58.4% women) aged 54-105 (mean = 72.7) years and without dementia at baseline. Studies had 2-15 years of follow-up. The risk factors investigated were age, sex, education, alcohol consumption, anxiety, apolipoprotein E ε4 allele (APOE*4) status, atrial fibrillation, blood pressure and pulse pressure, body mass index, cardiovascular disease, depression, diabetes, self-rated health, high cholesterol, hypertension, peripheral vascular disease, physical activity, smoking, and history of stroke. Associations with risk factors were determined for a global cognitive composite outcome (memory, language, processing speed, and executive functioning tests) and Mini-Mental State Examination score. Individual participant data meta-analyses of multivariable linear mixed model results pooled across cohorts revealed that for at least 1 cognitive outcome, age (B = -0.1, SE = 0.01), APOE*4 carriage (B = -0.31, SE = 0.11), depression (B = -0.11, SE = 0.06), diabetes (B = -0.23, SE = 0.10), current smoking (B = -0.20, SE = 0.08), and history of stroke (B = -0.22, SE = 0.09) were independently associated with poorer cognitive performance (p < 0.05 for all), and higher levels of education (B = 0.12, SE = 0.02) and vigorous physical activity (B = 0.17, SE = 0.06) were associated with better performance (p < 0.01 for both). Age (B = -0.07, SE = 0.01), APOE*4 carriage (B = -0.41, SE = 0.18), and diabetes (B = -0.18, SE = 0.10) were independently associated with faster cognitive decline (p < 0.05 for all). Different effects between Asian people and white people included stronger associations for Asian people between ever smoking and poorer cognition (group by risk factor interaction: B = -0.24, SE = 0.12), and between diabetes and cognitive decline (B = -0.66, SE = 0.27; p < 0.05 for both). Limitations of our study include a loss or distortion of risk factor data with harmonization, and not investigating factors at midlife. These results suggest that education, smoking, physical activity, diabetes, and stroke are all modifiable factors associated with cognitive decline. If these factors are determined to be causal, controlling them could minimize worldwide levels of cognitive decline. However, any global prevention strategy may need to consider ethno-regional differences.

Mild cognitive impairment (MCI) represents a transitional stage between healthy aging and dementia, and affects 10–15% of the population over the age of 65. The failure of drug trials in Alzheimer’s disease (AD) treatment has shifted researchers’ focus toward delaying progression from MCI to dementia, which would reduce the prevalence and costs of dementia profoundly. Diagnostic criteria for MCI increasingly emphasize the need for positive biomarkers to detect preclinical AD. The phenomenology of MCI comprises lower quality-of-life, greater symptoms of depression, and avoidant coping strategies including withdrawal from social engagement. Neurobiological features of MCI are hypoperfusion and hypometabolism in temporoparietal cortices, medial temporal lobe atrophy particularly in rhinal cortices, elevated tau and phosphorylated tau and decreased Aβ 42 in cerebrospinal fluid, and brain Aβ 42 deposition. Elevated tau can be identified in MCI, particularly in the entorhinal cortex, using positron emission tomography, and analysis of signal complexity using electroencephalography or magnetoencephalography holds promise as a biomarker. Assessment of MCI also relies on cognitive screening and neuropsychological assessment, but there is an urgent need for standardized cognitive tests to capitalize on recent discoveries in cognitive neuroscience that may lead to more sensitive measures of MCI. Cholinesterase inhibitors are frequently prescribed for MCI, despite the lack of evidence for their efficacy. Exercise and diet interventions hold promise for increasing reserve in MCI, and group psychoeducational programs teaching practical memory strategies appear effective. More work is needed to better understand the phenomenology and neurobiology of MCI, and how best to assess it and delay progression to dementia.

Plasma tau phosphorylated at threonine 217 (p-tau217) and plasma tau phosphorylated at threonine 181 (p-tau181) are associated with Alzheimer's disease tau pathology. We compared the diagnostic value of both biomarkers in cognitively unimpaired participants and patients with a clinical diagnosis of mild cognitive impairment, Alzheimer's disease syndromes, or frontotemporal lobar degeneration (FTLD) syndromes. In this retrospective multicohort diagnostic performance study, we analysed plasma samples, obtained from patients aged 18–99 years old who had been diagnosed with Alzheimer's disease syndromes (Alzheimer's disease dementia, logopenic variant primary progressive aphasia, or posterior cortical atrophy), FTLD syndromes (corticobasal syndrome, progressive supranuclear palsy, behavioural variant frontotemporal dementia, non-fluent variant primary progressive aphasia, or semantic variant primary progressive aphasia), or mild cognitive impairment; the participants were from the University of California San Francisco (UCSF) Memory and Aging Center, San Francisco, CA, USA, and the Advancing Research and Treatment for Frontotemporal Lobar Degeneration Consortium (ARTFL; 17 sites in the USA and two in Canada). Participants from both cohorts were carefully characterised, including assessments of CSF p-tau181, amyloid-PET or tau-PET (or both), and clinical and cognitive evaluations. Plasma p-tau181 and p-tau217 were measured using electrochemiluminescence-based assays, which differed only in the biotinylated antibody epitope specificity. Receiver operating characteristic analyses were used to determine diagnostic accuracy of both plasma markers using clinical diagnosis, neuropathological findings, and amyloid-PET and tau-PET measures as gold standards. Difference between two area under the curve (AUC) analyses were tested with the Delong test. Data were collected from 593 participants (443 from UCSF and 150 from ARTFL, mean age 64 years [SD 13], 294 [50%] women) between July 1 and Nov 30, 2020. Plasma p-tau217 and p-tau181 were correlated (r=0·90, p<0·0001). Both p-tau217 and p-tau181 concentrations were increased in people with Alzheimer's disease syndromes (n=75, mean age 65 years [SD 10]) relative to cognitively unimpaired controls (n=118, mean age 61 years [SD 18]; AUC=0·98 [95% CI 0·95–1·00] for p-tau217, AUC=0·97 [0·94–0·99] for p-tau181; pdiff=0·31) and in pathology-confirmed Alzheimer's disease (n=15, mean age 73 years [SD 12]) versus pathologically confirmed FTLD (n=68, mean age 67 years [SD 8]; AUC=0·96 [0·92–1·00] for p-tau217, AUC=0·91 [0·82–1·00] for p-tau181; pdiff=0·22). P-tau217 outperformed p-tau181 in differentiating patients with Alzheimer's disease syndromes (n=75) from those with FTLD syndromes (n=274, mean age 67 years [SD 9]; AUC=0·93 [0·91–0·96] for p-tau217, AUC=0·91 [0·88–0·94] for p-tau181; pdiff=0·01). P-tau217 was a stronger indicator of amyloid-PET positivity (n=146, AUC=0·91 [0·88–0·94]) than was p-tau181 (n=214, AUC=0·89 [0·86–0·93]; pdiff=0·049). Tau-PET binding in the temporal cortex was more strongly associated with p-tau217 than p-tau181 (r=0·80 vs r=0·72; pdiff<0·0001, n=230). Both p-tau217 and p-tau181 had excellent diagnostic performance for differentiating patients with Alzheimer's disease syndromes from other neurodegenerative disorders. There was some evidence in favour of p-tau217 compared with p-tau181 for differential diagnosis of Alzheimer's disease syndromes versus FTLD syndromes, as an indication of amyloid-PET-positivity, and for stronger correlations with tau-PET signal. Pending replication in independent, diverse, and older cohorts, plasma p-tau217 and p-tau181 could be useful screening tools to identify individuals with underlying amyloid and Alzheimer's disease tau pathology. US National Institutes of Health, State of California Department of Health Services, Rainwater Charitable Foundation, Michael J Fox foundation, Association for Frontotemporal Degeneration, Alzheimer's Association.

Background The Apolipoprotein E ε4 allele (i.e. ApoE4) is the strongest genetic risk factor for sporadic Alzheimer’s disease (AD). TREM2 (i.e. Triggering receptor expressed on myeloid cells 2) is a microglial transmembrane protein brain that plays a central role in microglia activation in response to AD brain pathologies. Whether higher TREM2-related microglia activity modulates the risk to develop clinical AD is an open question. Thus, the aim of the current study was to assess whether higher sTREM2 attenuates the effects of ApoE4-effects on future cognitive decline and neurodegeneration. Methods We included 708 subjects ranging from cognitively normal (CN, n  = 221) to mild cognitive impairment (MCI, n  = 414) and AD dementia ( n  = 73) from the Alzheimer’s disease Neuroimaging Initiative. We used linear regression to test the interaction between ApoE4-carriage by CSF-assessed sTREM2 levels as a predictor of longitudinally assessed cognitive decline and MRI-assessed changes in hippocampal volume changes (mean follow-up of 4 years, range of 1.7-7 years). Results Across the entire sample, we found that higher CSF sTREM2 at baseline was associated with attenuated effects of ApoE4-carriage (i.e. sTREM2 x ApoE4 interaction) on longitudinal global cognitive ( p  = 0.001, Cohen’s f 2  = 0.137) and memory decline ( p  = 0.006, Cohen’s f 2  = 0.104) as well as longitudinally assessed hippocampal atrophy ( p  = 0.046, Cohen’s f 2  = 0.089), independent of CSF markers of primary AD pathology (i.e. Aβ 1–42 , p-tau 181 ). While overall effects of sTREM2 were small, exploratory subanalyses stratified by diagnostic groups showed that beneficial effects of sTREM2 were pronounced in the MCI group. Conclusion Our results suggest that a higher CSF sTREM2 levels are associated with attenuated ApoE4-related risk for future cognitive decline and AD-typical neurodegeneration. These findings provide further evidence that TREM2 may be protective against the development of AD.

IntroductionSubjective cognitive decline (SCD) can represent a preclinical stage of Alzheimer’s disease. Diffusion tensor imaging (DTI) could aid an early diagnosis, yet only few monocentric DTI studies in SCD have been conducted, reporting heterogeneous results. We investigated microstructural changes in SCD in a larger, multicentric cohort.Methods271 participants with SCD, mild cognitive impairment (MCI) or Alzheimer’s dementia (AD) and healthy controls (CON) were included, recruited prospectively at nine centers of the observational DELCODE study. DTI was acquired using identical protocols. Using voxel-based analyses, we investigated fractional anisotropy (FA), mean diffusivity (MD) and mode (MO) in the white matter (WM). Discrimination accuracy was determined by cross-validated elastic-net penalized regression. Center effects were explored using variance analyses.ResultsMO and FA were lower in SCD compared to CON in several anterior and posterior WM regions, including the anterior corona radiata, superior and inferior longitudinal fasciculus, cingulum and splenium of the corpus callosum (p < 0.01, uncorrected). MD was higher in the superior and inferior longitudinal fasciculus, cingulum and superior corona radiata (p < 0.01, uncorrected). The cross-validated accuracy for discriminating SCD from CON was 67% (p < 0.01). As expected, the AD and MCI groups had higher MD and lower FA and MO in extensive regions, including the corpus callosum and temporal brain regions. Within these regions, center accounted for 3–15% of the variance.ConclusionsDTI revealed subtle WM alterations in SCD that were intermediate between those in MCI and CON and may be useful to detect individuals with an increased risk for AD in clinical studies.

Dynamic Functional Connectivity (dFC) in the resting state (rs) is considered as a correlate of cognitive processing. Describing dFC as a flow across morphing connectivity configurations, our notion of dFC speed quantifies the rate at which FC networks evolve in time. Here we probe the hypothesis that variations of rs dFC speed and cognitive performance are selectively interrelated within specific functional subnetworks. In particular, we focus on Sleep Deprivation (SD) as a reversible model of cognitive dysfunction. We found that whole-brain level (global) dFC speed significantly slows down after 24h of SD. However, the reduction in global dFC speed does not correlate with variations of cognitive performance in individual tasks, which are subtle and highly heterogeneous. On the contrary, we found strong correlations between performance variations in individual tasks –including Rapid Visual Processing (RVP, assessing sustained visual attention)– and dFC speed quantified at the level of functional sub-networks of interest. Providing a compromise between classic static FC (no time) and global dFC (no space), modular dFC speed analyses allow quantifying a different speed of dFC reconfiguration independently for sub-networks overseeing different tasks. Importantly, we found that RVP performance robustly correlates with the modular dFC speed of a characteristic frontoparietal module. •Sleep Deprivation (SD) slows down the random walk in FC space implemented by Dynamic Functional Connectivity (dFC) at rest.•Whole-brain level slowing of dFC speed does not selectively correlate with fine and task-specific changes in performance.•We quantify dFC speed separately for different link-based modules coordinated by distinct regional “meta-hubs”.•Modular dFC speed variations capture subtle and task-specific variations of cognitive performance induced by SD.

Multifactorial mechanisms underlying late-onset Alzheimer’s disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system’s integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions. Late-onset Alzheimer's disease (LOAD) is a complex multi-factorial disorder. Here, the authors perform a data-driven analysis of LOAD progression, including multimodal brain imaging, plasma and CSF biomarkers, and find vascular dysfunction is among the earliest and strongest altered events.