Behavior. ANCOVA was used to model encoding task accuracy and recognition accuracy by subject age, and encoding task RTs by subsequent memory (i.e., hit versus miss), age, and recognition accuracy. Recognition test RTs were modeled in a 2 (studied versus new scene) by 2 (correct versus incorrect response) group design, with age and recognition accuracy as covariates. Effects were considered significant if they passed the Bonferroni-corrected threshold for multiple comparisons [i.e., Bonferroni-corrected α = 0.05/m (m is the number of main + interaction effects)] (47). In cases where effects passed the uncorrected threshold of 0.05, but not the corrected threshold, we used stepwise multiple regression to systematically eliminate variables and reveal independent effects. All ANCOVA and post hoc regression analyses were performed using the MATLAB toolbox MANCOVAN version 1.16 (www.mathworks.com/matlabcentral/fileexchange/27014-mancovan).

Electrode selection. Statistical analysis of task-induced effects was performed per subject by standardizing the encoding power outputs on the pretrial baseline via bootstrapping. Baseline power values were pooled into a single time series for each electrode and frequency, from which we randomly selected and averaged r data points (r is the number of trials in that subject’s data set). This step was repeated 1000 times to create normal distributions of electrode/frequency-resolved pretrial baseline data. Encoding raw power data were z scored on the pretrial baseline distributions. This procedure adjusts the power outputs to correct for the 1/f power scaling law and reveals activity that is induced, with statistical significance, by the presentation of a scene [for a similar approach, see (32, 48, 49)]. Task-responsive electrodes were defined by all-trial mean significant increases in power that were sustained for at least 100 ms in at least two contiguous frequency bands during the 3-s scene presentation epoch, adjusted for multiple comparisons over electrodes and frequencies at the per-subject FDR-corrected α = 0.05 [e.g., (32, 33)]. A total of 301 lateral frontal electrodes were selected for analysis, with a mean of 18 ± 7 (range, 3 to 27) per subject.

Baseline analysis. The Wilcoxon signed-rank test was used to assess spectral subsequent memory effects (i.e., hit versus miss trials) per subject during the pretrial baseline. Raw power values were averaged over the −450 to −150 ms pretrial interval and tested per electrode and frequency. Effects were adjusted at the per-subject FDR-corrected α = 0.05.

Power latency analysis. The predictive relationship between the timing of frontal activity and encoding responses was assessed per subject using linear correlation with FDR correction for multiple comparisons. The per-trial latency of peak task-induced power, prior to the encoding judgment, was indexed per frequency and electrode. The latency of peak activity was defined as the moment of maximal activity occurring at any time point between the onset of the stimulus and the onset of the verbal response. Then, Pearson’s correlation was used to test whether the indexed latency of peak power predicted the latency of the encoding response. Effects were adjusted at the per-subject FDR-corrected α = 0.05 to correct for multiple comparisons over electrodes and frequencies.

We then used group-level ANCOVA and post hoc stepwise multiple regression to test whether individual mean latency of peak activity, per frontal subregion, predicted recognition accuracy. Peak latency data were submitted to ANCOVAs, with subsequent memory (i.e., hit versus miss) as the grouping variable and subject age and mean recognition accuracy as covariates. In cases where effects passed the uncorrected but not Bonferroni-corrected threshold of 0.05 (47), we used stepwise multiple regression to systematically eliminate variables and reveal independent effects.

Spectral subsequent memory analysis. Task-induced power data were tested for subsequent memory effects per subject using nonparametric Z tests with FDR correction for multiple comparisons. First, trials with RTs > 3 s were discarded, and all remaining power data segments (0 to +3 s from scene onset) were shifted per trial on the time axis so that the response onset was aligned across trials. The empirical subsequent memory effect was defined as mean z-hit power − mean z-miss power for each time, frequency, and electrode data point. Then, subsequent hit/miss labels were randomly shuffled using the Monte Carlo method and the subsequent memory effect was recalculated; this procedure was repeated 10,000 times to create a normal distribution of chance effects. Observed subsequent memory effects were considered significant if the empirical effect was significant at the two-tailed α = 0.05 (i.e., |z-hit − z-miss| > 1.96), and fewer than 5% of randomizations yielded a larger effect (corrected α = 0.05). Last, we thresholded both the Z test outputs and time-frequency representations of power at the per-subject FDR-corrected α = 0.05 to correct for multiple comparisons over electrodes, frequencies, and time points. Observed subsequent memory effects were considered significant where positive effects intersected with significant z-hit power values, and negative effects intersected with significant z-miss power values.

Linear mixed-effects models were used to test the temporal dynamics of subsequent memory effects on the group level (48). Z test outputs were pooled within each subject across intraregional electrodes and four 1-s epochs from response onset: −1500 to −500 ms, −1000 to 0 ms, −500 to +500 ms, and 0 to +1000 ms. We computed the mean of all significant subsequent memory effects and submitted the outputs to statistical testing per region, with time epochs as fixed effects and subjects as random effects. We then submitted the same outputs to ANCOVA per frontal subregion and epoch, with subject age and mean recognition accuracy as covariates to assess individual differences.

Inter-regional power lead/lag analysis. Dynamic flow of task-induced power was quantified per subject between all pairs of electrodes in adjacent frontal subregions using nonparametric partial correlations with FDR correction for multiple comparisons. First, trials with RTs > 3 s were discarded and all remaining power data segments (0 to +3 s from scene onset) were shifted per trial on the time axis so that the response onset was aligned across trials, and the trial-wise means were calculated for hit and miss trials. Then, Spearman’s rank correlation was computed for each time, frequency, and electrode-pair data point between 500-ms power data segments at each electrode A-B pair by sliding electrode B in 25-ms increments at latencies from 0 to 475 ms following electrode A. For comparable approaches, see (32, 34). We also partialled out the all-electrode mean power at each time-lag increment to minimize the confounding impact of a common reference scheme on connectivity estimates (50). Observed correlation coefficients were considered significant if the P value was significant at the positive-tailed α = 0.05, adjusted at the per-subject FDR-corrected α = 0.05. Power lag data were then tested for subsequent memory effects using Fisher’s Z tests with FDR correction for multiple comparisons (i.e., FDR-corrected α = 0.05). Observed subsequent memory effects were considered significant where positive effects intersected with significant P values on hit trials and negative effects intersected with significant P values on miss trials.

Linear mixed-effects models were again used to test the temporal dynamics of subsequent memory effects on the group level (48). Z test outputs were pooled within each subject across inter-regional electrodes and temporal lags from 25 to 475 ms in four 1-s epochs from −1500 to +500 ms relative to the response onset. We computed the mean of all significant subsequent memory effects in each direction (i.e., electrodes A-to-B and B-to-A) and submitted the outputs to statistical testing per region, with direction and time epochs as fixed effects and subjects as random effects. Effects were considered significant at the Bonferroni-corrected threshold of 0.05 (47). We then submitted the same outputs to ANCOVA per region and 1-s epoch, with direction as the grouping variable and subject age and mean recognition accuracy as covariates. When effects passed the uncorrected but not Bonferroni-corrected threshold, we used stepwise multiple regression to systematically eliminate variables and reveal independent effects.

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