Methods: We use the National Alzheimer’s Coordinating Center’s (NACC) Uniform Data Set (UDS) combined with NACC’s Biomarker and Imaging Data Sets. Individual cases are selected for inclusion if they have magnetic resonance imaging (MRI) data collected ± 90 days of interview (n = 1013). Individuals with birthdates from January 1961 to present are excluded (n = 202), resulting a total sample size of 811. The sample consists of 495 females (61%) and 316 males (39%). Most participants are Caucasian (85.3%; n = 692). African-Americans represent 11.8% (n = 96) of the sample. Missing data is addressed with multiple imputation.
We create three birth cohorts based on birthdate: Pre-Depression (before November 1929; n = 224), Depression (between November 1929 and December 1941; n = 330), and Baby Boom (January 1942 to December 1960; n = 257). Multiple regression is conducted in SPSS (version 23.0).
Results: As expected, total hippocampal volume decreases with age regardless of Alzheimer’s diagnosis. Years of education is not a statistically significant predictor of hippocampal volume in this model.
The reference group for this analysis is individuals born before the Great Depression. Individuals born during the Great Depression have an increase in mean hippocampal volume by 0.35 cc (p < 0.001; 95% CI [0.28, 0.41]), when controlling for sex and Alzheimer’s diagnosis. Baby Boomers have an increase in mean hippocampal volume by 0.76 cc (p < 0.001; 95% CI [0.69, 0.83]), when controlling for sex and Alzheimer’s diagnosis. Individuals with a positive Alzheimer’s diagnosis have a lower mean hippocampal volume than those who do not, when controlling for sex and birth cohort (B = -0.80, p < 0.001, 95% CI [-0.86, -0.75]).
Conclusions and Implications: Some evidence of differences in hippocampal volume by cohort is reflected. Primary and epigenetic effects of chronic stress, food security, access to education, and other such factors may explain these generational differences, but further research is required. Inclusion of neuroscientific data in this way can bridge gaps in interdisciplinary collaboration and enhance social workers’ understanding and conceptualization of aging.