Several concepts, which in the aggregate get might be used to account for "resilience" against age- and disease-related changes, have been the subject of much research. These include brain reserve, cognitive reserve, and brain maintenance. However, different investigators have use these terms in different ways, and there has never been an attempt to arrive at consensus on the definition of these concepts. Furthermore, there has been confusion regarding the measurement of these constructs and the appropriate ways to apply them to research. Therefore the reserve, resilience, and protective factors professional interest area, established under the auspices of the Alzheimer's Association, established a whitepaper workgroup to develop consensus definitions for cognitive reserve, brain reserve, and brain maintenance. The workgroup also evaluated measures that have been used to implement these concepts in research settings and developed guidelines for research that explores or utilizes these concepts. The workgroup hopes that this whitepaper will form a reference point for researchers in this area and facilitate research by supplying a common language.

In order to understand the brain networks that mediate cognitive reserve, we explored the relationship between subjects' network expression during the performance of a memory test and an index of cognitive reserve. Using H2(15)O positron emission tomography, we imaged 17 healthy older subjects and 20 young adults while they performed a serial recognition memory task for nonsense shapes under two conditions: low demand, with a unique shape presented in each study trial; and titrated demand, with a study list size adjusted so that each subject recognized shapes at 75% accuracy. A factor score that summarized years of education, and scores on the NART and the WAIS-R Vocabulary subtest was used as an index of cognitive reserve. The scaled subprofile model was used to identify a set of functionally connected regions (or topography) that changed in expression across the two task conditions and was differentially expressed by the young and elderly subjects. The regions most active in this topography consisted of right hippocampus, posterior insula, thalamus, and right and left operculum; we found concomitant deactivation in right lingual gyrus, inferior parietal lobe and association cortex, left posterior cingulate, and right and left calcarine cortex. Young subjects with higher cognitive reserve showed increased expression of the topography across the two task conditions. Because this topography, which is responsive to increased task demands, was differentially expressed as a function of reserve level, it may represent a neural manifestation of innate or acquired reserve. In contrast, older subjects with higher cognitive reserve showed decreased expression of the topography across tasks. This suggests some functional reorganization of the network used by the young subjects. Thus, for the old subjects this topography may represent an altered, compensatory network that is used to maintain function in the face of age-related physiological changes.

This study investigated the relationship between education and physical activity and the difference between a physiological prediction of age and chronological age. Cortical and subcortical grey matter regional volumes were calculated from 331 healthy adults (range: 19-79 years). Multivariate analyses identified a covariance pattern of brain volumes best predicting chronological age (CA)(R2 = 47%). Individual expression of this brain pattern served as a physiologic measure of brain age (BA). The difference between CA and BA was predicted by education and self-report measures of physical activity. Education and the daily number of flights of stairs climbed were the only two significant predictors of decreased brain age. Effect sizes demonstrated that brain age decreased by 0.95 years for each year of education and by 0.58 years for one additional daily FOSC. Effects of education and FOSC on regional brain volume were largely driven by temporal and subcortical volumes. These results demonstrate that higher levels of education and daily FOSC are related to larger brain volume than predicted by chronological age which supports the utility of regional grey matter volume as a biomarker of healthy brain aging.

At any given level of clinical disease severity, there is a greater degree of brain pathologic involvement in patients with AD who have more engagement in activities, even when education and IQ are taken into account. This may suggest that interindividual differences in lifestyle may affect cognitive reserve by partially mediating the relationship between brain damage and the clinical manifestation of AD.

Among older adults, MeDi adherence was associated with less brain atrophy, with an effect similar to 5 years of aging. Higher fish and lower meat intake might be the 2 key food elements that contribute to the benefits of MeDi on brain structure.

We performed univariate and multivariate discriminant analysis of FDG-PET scans to evaluate their ability to identify Alzheimer's disease (AD). FDG-PET scans came from two sources: 17 AD patients and 33 healthy elderly controls were scanned at the University of Michigan; 102 early AD patients and 20 healthy elderly controls were scanned at the Technical University of Munich, Germany. We selected a derivation sample of 20 AD patients and 20 healthy controls matched on age with the remainder divided into 5 replication samples. The sensitivity and specificity of diagnostic ADmarkers and threshold criteria from the derivation sample were determined in the replication samples. Although both univariate and multivariate analyses produced markers with high classification accuracy in the derivation sample, the multivariate marker's diagnostic performance in the replication samples was superior. Further, supplementary analysis showed its performance to be unaffected by the loss of key regions. Multivariate measures of AD utilize the covariance structure of imaging data and provide complementary, clinically relevant information that may be superior to univariate measures.

Eighteen subjects (ages 18-35) underwent event-related functional magnetic resonance imaging (efMRI) while performing a delayed-match-to-sample (DMS) task before and immediately after 48 h of sustained wakefulness. The DMS trial events were: a 3-s study period of either a one-, three-, or six-letter visual array; a 7-s retention interval; and a 3-s probe period, where a button press indicated whether the probe letter was in the study array. Ordinal Trend Canonical Variates Analysis (OrT CVA) was applied to the data from the probe period for trials with six-letter study lists prior to and immediately following sleep deprivation to find an activation pattern whose expression decreased with sleep deprivation in as many subjects as possible, while being present in both conditions. The first principal component of the OrT analysis identified a covariance pattern whose expression decreased as a function of sleep deprivation in 17 of 18 subjects (p<0.001). While overall expression of the pattern showed a systematic decrease with sleep deprivation, the brain regions that make up the pattern show covarying increases and decreases in activation. Regions that decreased their activation were noted in the parietal (BA 7 and 40), temporal (BA 37, 38 and 39) and occipital (BA 18 and 19) lobes; regions that increased their activation were noted in the cerebellum, basal ganglia, thalamus and the anterior cingulate gyrus (BA 32). The reduction in pattern expression with sleep deprivation for each subject was related to the change in performance on the DMS task. Subject decreases in pattern expression were correlated with reductions in recognition accuracy (p<0.05), increased intra-individual variability in reaction time (p<0.005) and increased lapsing (p<0.005).

Structural magnetic resonance imaging (MRI) studies have shown dramatic age-associated changes in grey and white matter volume, but typically use univariate analyses that do not explicitly test the interrelationship among brain regions. The current study used a multivariate approach to identify covariance patterns of grey and white matter tissue density to distinguish older from younger adults. A second aim was to examine whether the expression of the age-associated covariance topographies is related to performance on cognitive tests affected by normal aging. Eighty-four young (mean age=24.0) and 29 older (mean age=73.1) participants were scanned with a 1.5T MRI machine and assessed with a cognitive battery. Images were spatially normalized and segmented to produce grey and white matter density maps. A multivariate technique, based on the subprofile scaling model, was used to capture sources of between- and within-group variation to produce a linear combination of principal components that represented a "pattern" or "network" that best discriminated between the two age groups. Univariate analyses were also conducted with statistical parametric maps. Grey and white matter covariance patterns were identified that reliably discriminated between the groups with greater than 0.90 sensitivity and specificity. The identified patterns were similar for the univariate and multivariate techniques, and involved widespread regions of the cortex and subcortex. Age and the expression of both patterns were significantly associated with performance on tests of attention, language, memory, and executive functioning. The results suggest that identifiable networks of grey and white matter regions systematically decline with age and that pattern expression is linked to age-related cognitive decline.