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Objective To investigate the relationship between the ATN classification system (amyloid, tau, neurodegeneration) and risk of dementia and cognitive decline in individuals with subjective cognitive decline (SCD). Methods We classified 693 participants with SCD (60 ± 9 years, 41% women, Mini-Mental State Examination score 28 ± 2) from the Amsterdam Dementia Cohort and Subjective Cognitive Impairment Cohort (SCIENCe) project according to the ATN model, as determined by amyloid PET or CSF β-amyloid (A), CSF p-tau (T), and MRI-based medial temporal lobe atrophy (N). All underwent extensive neuropsychological assessment. For 342 participants, follow-up was available (3 ± 2 years). As a control population, we included 124 participants without SCD. Results Fifty-six (n = 385) participants had normal Alzheimer disease (AD) biomarkers (A–T–N–), 27% (n = 186) had non-AD pathologic change (A–T–N+, A–T+N–, A–T+N+), 18% (n = 122) fell within the Alzheimer continuum (A+T–N–, A+T–N+, A+T+N–, A+T+N+). ATN profiles were unevenly distributed, with A–T+N+, A+T–N+, and A+T+N+ containing very few participants. Cox regression showed that compared to A–T–N–, participants in A+ profiles had a higher risk of dementia with a dose–response pattern for number of biomarkers affected. Linear mixed models showed participants in A+ profiles showed a steeper decline on tests addressing memory, attention, language, and executive functions. In the control group, there was no association between ATN and cognition. Conclusions Among individuals presenting with SCD at a memory clinic, those with a biomarker profile A–T+N+, A+T–N–, A+T+N–, and A+T+N+ were at increased risk of dementia, and showed steeper cognitive decline compared to A–T–N– individuals. These results suggest a future where biomarker results could be used for individualized risk profiling in cognitively normal individuals presenting at a memory clinic.

Cognitive reserve (CR) explains interindividual differences in the ability to maintain cognitive function in the presence of neuropathology. We developed a neuroimaging approach including a measure of brain atrophy and cognition to capture this construct. In a group of 511 Alzheimer's disease (AD) biomarker‐positive subjects in different stages across the disease spectrum, we performed 3T magnetic resonance imaging and predicted gray matter (GM) volume in each voxel based on cognitive performance (i.e. a global cognitive composite score), adjusted for age, sex, disease stage, premorbid brain size (i.e. intracranial volume) and scanner type. We used standardized individual differences between predicted and observed GM volume (i.e. W‐scores) as an operational measure of CR. To validate this method, we showed that education correlated with mean W‐scores in whole‐brain (r = −0.090, P < 0.05) and temporoparietal (r = −0.122, P < 0.01) masks, indicating that higher education was associated with more CR (i.e. greater atrophy than predicted from cognitive performance). In a voxel‐wise analysis, this effect was most prominent in the right inferior and middle temporal and right superior lateral occipital cortex (P < 0.05, corrected for multiple comparisons). Furthermore, survival analyses among subjects in the pre‐dementia stage revealed that the W‐scores predicted conversion to more advanced disease stages (whole‐brain: hazard ratio [HR] = 0.464, P < 0.05; temporoparietal: HR = 0.397, P < 0.001). Our neuroimaging approach captures CR with high anatomical detail and at an individual level. This standardized method is applicable to various brain diseases or CR proxies and can flexibly incorporate different neuroimaging modalities and cognitive parameters, making it a promising tool for scientific and clinical purposes. Hum Brain Mapp 38:4703–4715, 2017. © 2017 Wiley Periodicals, Inc.