Cognitive reserve (CR) prevents cognitive decline and delays neurodegeneration. Recent epidemiological evidence suggests that lifelong bilingualism may act as CR delaying the onset of dementia by ∼4.5 y. Much controversy surrounds the issue of bilingualism and its putative neuroprotective effects. We studied brain metabolism, a direct index of synaptic function and density, and neural connectivity to shed light on the effects of bilingualism in vivo in Alzheimer's dementia (AD). Eighty-five patients with probable AD and matched for disease duration (45 German-Italian bilingual speakers and 40 monolingual speakers) were included. Notably, bilingual individuals were on average 5 y older than their monolingual peers. In agreement with our predictions and with models of CR, cerebral hypometabolism was more severe in the group of bilingual individuals with AD. The metabolic connectivity analyses crucially supported the neuroprotective effect of bilingualism by showing an increased connectivity in the executive control and the default mode networks in the bilingual, compared with the monolingual, AD patients. Furthermore, the degree of lifelong bilingualism (i.e., high, moderate, or low use) was significantly correlated to functional modulations in crucial neural networks, suggesting both neural reserve and compensatory mechanisms. These findings indicate that lifelong bilingualism acts as a powerful CR proxy in dementia and exerts neuroprotective effects against neurodegeneration. Delaying the onset of dementia is a top priority of modern societies, and the present in vivo neurobiological evidence should stimulate social programs and interventions to support bilingual or multilingual education and the maintenance of the second language among senior citizens.bilingualism | Alzheimer's dementia | fluorine-18-fluorodeoxyglucose PET | brain reserve | brain metabolic connectivity

Tau pathology, neuroinflammation, and neurodegeneration are key aspects of Alzheimer’s disease. Understanding whether these features predict cognitive decline, alone or in combination, is crucial to develop new prognostic measures and enhanced stratification for clinical trials. Here, we studied how baseline assessments of in vivo tau pathology (measured by 18F-AV-1451 PET), neuroinflammation (measured by 11C-PK11195 PET) and brain atrophy (derived from structural MRI) predicted longitudinal cognitive changes in patients with Alzheimer’s disease pathology. Twenty-six patients (n = 12 with clinically probable Alzheimer’s dementia and n = 14 with amyloid-positive mild cognitive impairment) and 29 healthy control subjects underwent baseline assessment with 18F-AV-1451 PET, 11C-PK11195 PET, and structural MRI. Cognition was examined annually over the subsequent 3 years using the revised Addenbrooke’s Cognitive Examination. Regional grey matter volumes, and regional binding of 18F-AV-1451 and 11C-PK11195 were derived from 15 temporo-parietal regions characteristically affected by Alzheimer’s disease pathology. A principal component analysis was used on each imaging modality separately, to identify the main spatial distributions of pathology. A latent growth curve model was applied across the whole sample on longitudinal cognitive scores to estimate the rate of annual decline in each participant. We regressed the individuals’ estimated rate of cognitive decline on the neuroimaging components and examined univariable predictive models with single-modality predictors, and a multi-modality predictive model, to identify the independent and combined prognostic value of the different neuroimaging markers. Principal component analysis identified a single component for the grey matter atrophy, while two components were found for each PET ligand: one weighted to the anterior temporal lobe, and another weighted to posterior temporo-parietal regions. Across the whole-sample, the single-modality models indicated significant correlations between the rate of cognitive decline and the first component of each imaging modality. In patients, both stepwise backward elimination and Bayesian model selection revealed an optimal predictive model that included both components of 18F-AV-1451 and the first (i.e. anterior temporal) component for 11C-PK11195. However, the MRI-derived atrophy component and demographic variables were excluded from the optimal predictive model of cognitive decline. We conclude that temporo-parietal tau pathology and anterior temporal neuroinflammation predict cognitive decline in patients with symptomatic Alzheimer’s disease pathology. This indicates the added value of PET biomarkers in predicting cognitive decline in Alzheimer’s disease, over and above MRI measures of brain atrophy and demographic data. Our findings also support the strategy for targeting tau and neuroinflammation in disease-modifying therapy against Alzheimer’s disease.

ObjectivesThis longitudinal study compared emerging plasma biomarkers for neurodegenerative disease between controls, patients with Alzheimer’s disease (AD), Lewy body dementia (LBD), frontotemporal dementia (FTD) and progressive supranuclear palsy (PSP).MethodsPlasma phosphorylated tau at threonine-181 (p-tau181), amyloid beta (Αβ)42, Aβ40, neurofilament light (NfL) and glial fibrillar acidic protein (GFAP) were measured using highly sensitive single molecule immunoassays (Simoa) in a multicentre cohort of 300 participants (controls=73, amyloid positive mild cognitive impairment (MCI+) and AD dementia=63, LBD=117, FTD=28, PSP=19). LBD participants had known positron emission tomography (PET)-Aβ status.ResultsP-tau181 was elevated in MCI+AD compared with all other groups. Aβ42/40 was lower in MCI+AD compared with controls and FTD. NfL was elevated in all dementias compared with controls while GFAP was elevated in MCI+AD and LBD. Plasma biomarkers could classify between MCI+AD and controls, FTD and PSP with high accuracy but showed limited ability in differentiating MCI+AD from LBD. No differences were detected in the levels of plasma biomarkers when comparing PET-Aβ positive and negative LBD. P-tau181, NfL and GFAP were associated with baseline and longitudinal cognitive decline in a disease specific pattern.ConclusionThis large study shows the role of plasma biomarkers in differentiating patients with different dementias, and at monitoring longitudinal change. We confirm that p-tau181 is elevated in MCI+AD, versus controls, FTD and PSP, but is less accurate in the classification between MCI+AD and LBD or detecting amyloid brain pathology in LBD. NfL was elevated in all dementia groups, while GFAP was elevated in MCI+AD and LBD.

Cognitive reserve (CR) and brain reserve (BR) are protective factors against age-associated cognitive decline and neurodegenerative disorders. Very limited evidence exists about gender effects on brain aging and on the effect of CR on brain modulation in healthy aging and Alzheimer's Dementia (AD). We investigated gender differences in brain metabolic activity and resting-state network connectivity, as measured by F-FDG-PET, in healthy aging and AD, also considering the effects of education and occupation. The clinical and imaging data were retrieved from large datasets of healthy elderly subjects (HE) (225) and AD patients (282). In HE, males showed more extended age-related reduction of brain metabolism than females in frontal medial cortex. We also found differences in brain modulation as metabolic increases induced by education and occupation, namely in posterior associative cortices in HE males and in the anterior limbic-affective and executive networks in HE females. In AD patients, the correlations between education and occupation levels and brain hypometabolism showed gender differences, namely a posterior temporo-parietal association in males and a frontal and limbic association in females, indicating the involvement of different networks. Finally, the metabolic connectivity in both HE and AD aligned with these results, suggesting greater efficiency in the posterior default mode network for males, and in the anterior frontal executive network for females. The basis of these brain gender differences in both aging and AD, obtained exploring cerebral metabolism, metabolic connectivity and the effects of education and occupation, is likely at the intersection between biological and sociodemographic factors. Hum Brain Mapp 38:4212-4227, 2017. © 2017 Wiley Periodicals, Inc.

Background: The peak width of skeletonized mean diffusivity (PSMD) has been proposed as a fully automated imaging marker of relevance to cerebral small vessel disease (SVD). We assessed PSMD in relation to conventional SVD markers, global measures of neurodegeneration, and cognition.Methods: 145 participants underwent 3T brain MRI and cognitive assessment. 112 were patients with mild cognitive impairment, Alzheimer's disease, progressive supranuclear palsy, dementia with Lewy bodies, or frontotemporal dementia. PSMD, SVD burden [white matter hyperintensities (WMH), enlarged perivascular spaces (EPVS), microbleeds, lacunes], average mean diffusivity (MD), gray matter (GM), white matter (WM), and total intracranial volume were quantified. Robust linear regression was conducted to examine associations between variables. Dominance analysis assessed the relative importance of markers in predicting various outcomes. Regional analyses examined spatial overlap between PSMD and WMH.Results: PSMD was associated with global and regional SVD measures, especially WMH and microbleeds. Dominance analysis demonstrated that among SVD markers, WMH was the strongest predictor of PSMD. Furthermore, PSMD was more closely associated to WMH than with GM and WM volumes. PSMD was associated with WMH across all regions, and correlations were not significantly stronger in corresponding regions (e.g., frontal PSMD and frontal WMH) compared to non-corresponding regions. PSMD outperformed all four conventional SVD markers and MD in predicting cognition, but was comparable to GM and WM volumes.Discussion: PSMD was robustly associated with established SVD markers. This new measure appears to be a marker of diffuse brain injury, largely due to vascular pathology, and may be a useful and convenient metric of overall cerebrovascular burden.