A cutoff value of 1. Participants with one or more elevated AD-related pathology markers were categorized as positive for AD-related pathology. All incident measures represented a binary distinction as to whether a participant had either recently or remotely experienced the particular health issue or had never experienced it that is, absent at the time of their first scan. Traumatic brain-injury incidents were categorized and included in analyses as a categorical variable.
Ten participants were missing traumatic brain-injury-incident data. Depressive symptoms were measured by the GDS and included as a continuous variable in the analyses The scale has a possible range from 0 to 15 0—4, no depression; 5—8, mild depression; 9—11, moderate depression; 12—15, severe depression.
The present sample had a GDS score ranging from 0 to 8, with nine participants scoring between 5 and 8. One participant was missing GDS data. If an occupation listed had no direct match in the US census occupation code, the occupation code for the most related job was used. Two independent coders C. As such, SEI values were available for of participants. Instead, 5-year ACS data from to were used because they encompass the median scan date across all available scanning sessions median scan date, Five-year ACS estimates were chosen over 3-year or 1-year ACS estimates because they covered a greater proportion of zip codes that is, more areas are missing from 3-year or 1-year data and used larger sample sizes to determine estimates.
Not every zip code had 5-year ACS estimates available; therefore, some participants with zip code data did not have a matching neighborhood median household income estimate; thus they were excluded from the comparison in Fig. Neighborhood median household income data were estimated for of participants.
The national ADI represents the percentile ranking of neighborhood SES disadvantages, calculated using multiple variables for example, home value, gross rent, percent of families below poverty level, percent of households without a motor vehicle ADI scores were calculated on the block group level, for which data were first linked to nine-digit zip codes.
Nine-digit zip codes are sub-areas of five-digit zip codes. ADI was available for of participants. Because ADI data are aggregated from nine-digit zip codes and multiple measures of ACS data, more participant ADI data were available than median household income data. The present study used a longitudinal design, in which the measure of time that is, days from baseline was included as a within-participant variable.
Both analyses examining brain changes and cognitive impairment changes used a linear mixed-effects approach. First, in the analysis predicting longitudinal brain changes, linear mixed-effects models were used to examine how the dependent variable for example, primary analysis, brain system segregation; supplementary analyses, cortical thickness was predicted by time normalized time from baseline; within participant and its interaction with education group between participant and age at baseline between participant.
Age at each scan was not used as a measure of time to allow us to investigate the interaction between age and time. In the primary model, in-scanner head motion mean FD; within-participant variable collected at each longitudinal time point and sex between-participant variable were included as covariates. The linear mixed-effects model was calculated as follows:. Sex and its interaction with time was included to account for the fixed effect of sex on the random effect of time.
Multiple-comparison correction was applied when examining the two unique longitudinal brain change measures brain system segregation and cortical thickness. After correction, the comparison would require a P value of 0.
The interaction between age, time and education predicting brain system segregation surpassed this corrected P value, while the three-way interaction predicting cortical thickness remained insignificant.
All reported P values in the text are raw P values. In the analysis on longitudinal changes in cognitive impairment, similar linear mixed-effects models were constructed, with the dependent variable being CDR-SB or CDR section 1. Changes in brain system segregation were defined as the observed difference between brain system segregation measured from the last time point and baseline, as opposed to estimated changes from mixed models.
Although a substantial number of participants had more than two time points of resting-state data available, a notable portion only had two time points; therefore, estimates for these individuals were more prone to shrinkage that is, coefficients shifted more toward population values than within-participant least square estimates Accordingly, difference scores were used to ensure a consistent means to obtain within-person changes across all participants, which also meant that the conclusion from the model predicting CDR-SB was based on actual changes in brain system segregation instead of estimated changes from a separate mixed-effect model.
Furthermore, when predicting changes in CDR-SB, in addition to sex and average head motion baseline and last scan , time in days between baseline and the last MRI scan was included to account for the varying length of time available to quantify changes in brain system segregation. Education group was also included as a covariate in all models except when its interaction was tested. Normality of the dependent and independent variables was examined qualitatively using Q—Q plots, in which normality was relatively high for most variables.
The normality of brain system segregation increased through quadratic transformation although not substantially. We repeated the analyses using squared brain system segregation as the dependent variable, and the results presented in Table 2 remained qualitatively the same.
Following a previous study examining CDR-SB in relation to continuous time scale 25 , when quadratic time was included in the model predicting CDR-SB, the interaction between time, age at baseline and brain system-segregation change remained qualitatively the same. Residuals of the linear mixed-effects models were examined to ensure that they were not correlated with the fitted values of the fixed-effects portion of the model, nor were the residuals associated with the independent variables for example, age at baseline, education group.
Longitudinal RSFC changes in older participants 65 years and over within each education group were conducted by comparing their RSFC matrix from the first time point to that at the latest time point. Correlations among nodes from the same system were averaged together to form within-system blocks, and correlations among nodes across every pair of systems were averaged together to form between-system blocks.
Observed block-wise comparisons across time were computed using paired t -tests last scan versus first scan. Each permutation iteration shuffled the matrices within an education group across participants and time points, and the t value from a paired t -test using the permuted sample was recorded. For each block, the P value was calculated as the proportion of sampled statistics more extreme than the actual statistic.
A two-tailed P value less than 0. Linear mixed models and data visualization were carried out in R 3. Longitudinal spring-embedded graphs were generated using SoNIA 1. Visualization of nodes on cortical surfaces were generated using Connectome Workbench 1. Further information on research design is available in the Nature Research Reporting Summary linked to this article. Data include patient information and are private and unsuitable for public deposition.
Data requests are approved on a rolling basis. Requests involving neuroimaging data should be submitted to the ADRC for preliminary review before the leadership committee meeting director of the imaging core, T.
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