Participants. We utilized our prior sample of 80 chemotherapy-treated, primary breast cancer survivors (9). These women were age 35–73 years and had completed all primary treatments (surgery, radiation, chemotherapy) excluding hormone blockade at least 6 months before study enrollment (Table 1). Participants were free from disease and had no history of relapse or recurrence at the time of evaluation. Participants were excluded for neurologic, psychiatric, or medical conditions known to affect cognitive function. As noted above, participants were previously clustered into 3 biotypes (Biotype 1: “Low cognitive function”,, Biotype 2: “Cognitively resilient”, Biotype 3: “Moderately low cognitive function”) based on their individual patterns of functional connectivity within 8 functional brain networks, Biotype 1 (N = 36), Biotype 2 (N = 24) and Biotype 3 (N = 20) (9). Also included in the prior study were 82 non-cancer controls which we used again for comparison in the present study (Table 1). This study was approved by the Stanford University Institutional Review Board and all participants provided written informed consent.
Table 1
Biotype Characteristics. Data are shown as mean (standard deviation) unless otherwise noted.
| Biotype 1 (N = 36) | Biotype 2 (N = 24) | Biotype 3 (N = 20) | Controls (N = 82) | Stat | P value |
Age (years) | 49.30 (8.0) | 52.52 (6.4) | 52.16 (8.7) | 49.43 (13.2) | F = 0.747 | 0.525 |
Education (years) | 16.39 (2.4) | 17.13 (2.7) | 16.47 (2.2) | 16.94 (2.4) | F = 0.681 | 0.565 |
Racial/ethnic minority (%) | 36% | 13% | 12% | 16% | X2 = 8.93 | 0.030 |
Post-menopause (%) | 61% | 71% | 59% | 68% | X2 = 1.20 | 0.752 |
Stage at diagnosis (I,II,III %) | 30%,65%,5% | 21%,41%,38% | 18%,65%,17% | | X2 = 5.42 | 0.247 |
Radiotherapy (%) | 67% | 96% | 65% | | X2 = 7.91 | 0.019 |
Hormone blockade (%) | 61% | 75% | 64% | | X2 = 1.27 | 0.531 |
Time off-therapy (months) | 26.40 (18.7) | 49.67 (33.9) | 64.25 (82.1) | | F = 4.69 | 0.012 |
Neuroimaging Data Acquisitions. Functional magnetic resonance imaging (fMRI) data were obtained while participants rested with eyes closed using a T2* weighted gradient echo spiral pulse sequence: TR = 2000 ms, TE = 30 ms, flip angle = 80°, and 1 interleave, FOV = 22 cm, matrix = 64 × 64, in-plane resolution = 3.4375 mm2, number of volumes = 216 with a 3T GE Signa HDx whole body scanner (GE Medical Systems, Milwaukee, WI). A high-order shimming method was employed to reduce field heterogeneity. A high-resolution, 3D IR-prepared FSPGR anatomic scan was obtained: TR: 8.5, TE: minimum, flip: 15 degrees, TI: 400 ms, BW: +/−31.25 kHz, FOV: 22 cm, phase FOV: 0.75, slice thickness: 1.5 mm, 124 slices, 256 × 256 @ 1 NEX, scan time: 4:33 min.
Neurofunctional Stability. Spontaneous functional time series were measured from resting state fMRI data using Statistical Parametric Mapping 12 (28) and CONN 21a (29) implemented in Matlab v2021b (Mathworks, Inc, Natick, MA). Briefly, this involved realignment, coregistration with the segmented anatomic volume, spatial normalization, artifact detection (global signal = 3.0 standard deviations, motion = 1.0 mm, rotation = 0.05 mm), band-pass filtering (0.008–0.09 Hz), and correction of non-neuronal noise (30). We evaluated the temporal stability of spontaneous brain function by calculating the mean rescaled range Hurst exponent (31) across the entire brain. The windowing function was based on a data-derived natural number that possessed the largest number of divisors among all natural numbers in the time series interval. The Hurst exponent quantifies how correlated a time series is with itself, or how well it reflects elements of the baseline signal from both the recent and remote past. Although biotypes were originally defined based on functional connectivity, the Hurst exponent is based on dynamics of the time series and not the static correlation between regions. Hurst was not correlated with functional connectivity properties in breast cancer or controls (14), nor with the connectivity features used for biotyping (r < 0.097, p > 0.26).
Brain Age. Cortical thickness was measured from the anatomic volume using FreeSurfer (32). Briefly, non-brain tissue was removed followed by an automated spatial transformation, segmentation, intensity normalization, tessellation of gray/white-matter boundary, automated correction of topological defects and surface deformation to form the gray and white matter boundary. Cortical thickness was determined as the difference between the pial and white-matter surface (33, 34). We performed visual quality checks to ensure no major errors within the automated processing. Cortical age was then estimated using the Brain-Age Regression Analysis and Computation Utility Software (18).
APOE Genotype. Saliva samples were obtained from all participants using the Oragene DNA OG-250 collection kit (DNA Genotek, Kanata, Ontario). Genotyping was accomplished by polymerase chain reaction (PCR) fragment length polymorphism analysis with restricted fragment length polymorphisms. Twenty-three participants opted out of providing a saliva sample.
Psychoneurological Symptoms. Depression, anxiety, and fatigue were measured using the Clinical Assessment of Depression (CAD) (35). We previously evaluated the total score for this questionnaire and found no biotype differences (9), but here we examined subscales for a more granular assessment of symptoms. We measured sleep disruption using the Pittsburgh Sleep Quality Index (PSQI) (36).
Symptom-Based Categories. Our previous study examined cognitive symptoms between biotypes using a battery of five standardized cognitive tests (9). For the present study, we employed a common symptom-based classification to determine impairment categories (37). Specifically, a participant was classified as impaired if 2 or more cognitive test scores were 1.5 or more standard deviations below the test’s normative mean or at least one test score was 2 or more standard deviations below the normative mean. This classification will be referred to hereafter as symptom-type; 26% percent of participants were classified as having an impaired symptom-type and the remaining 74% had a non-impaired symptom-type (Table 2).
Table 2
Symptom-Type Characteristics. Data are shown as mean (standard deviation) unless otherwise noted.
| Unimpaired (N = 59) | Impaired (N = 21) | Controls (N = 82) | Stat | P value |
Age (years) | 51.72 (7.1) | 48.59 (9.0) | 49.43 (13.2) | F = 0.995 | 0.372 |
Education (years) | 16.88 (2.5) | 15.95 (2.5) | 16.94 (2.4) | F = 1.39 | 0.251 |
Racial/ethnic minority (%) | 12% | 55% | 16% | X2 = 19.0 | < 0.001 |
Post-menopause (%) | 65% | 60% | 68% | X2 0.531 | 0.767 |
Stage at diagnosis (I,II,III %) | 21%,56%,23% | 25%,55%,20% | | X2 = 0.161 | 0.923 |
Radiotherapy (%) | 75% | 75% | | X2 = 0.002 | 0.969 |
Hormone blockade (%) | 70% | 55% | | X2 = 1.52 | 0.217 |
Time off-therapy (months) | 48.5 (52.6) | 23.4 (7.5) | | F = 4.50 | 0.037 |
Statistical Analyses. Data were first inspected visually to confirm normality and homogeneity of variance. Brain network stability, cortical brain age, and psychoneurological symptoms were compared between the biotypes or symptom-types using ANOVA with Tukey correction for post-hoc pairwise biotype comparisons. APOE genotype was compared between the biotypes or symptom-types using a chi-squared test. CAD subscales were evaluated between biotypes or symptom-types using MANOVA followed by ANOVA with Tukey correction. PSQI scores were compared using ANOVA with Tukey correction.