Table S3.

Statistical tests, by figure

 Fig. no. Test Hypothesis Section/paragraph no. n Result Exact value Defined? Test statistic P value S1A Paired t test Group mean FC difference before global signal regression Results: Abnormal FC Patterns in Stroke, para. 1 52,326 Edges df = 52325 P = 0 t = 139.7 S1A Paired t test Group mean FC difference after global signal regression Results: Abnormal FC Patterns in Stroke , para. 1 52,326 Edges df = 52325 P = 0.76 t = -0.31 — ANOVA Homotopic FC by group, RSN, and head motion Results: Abnormal FC Patterns in Stroke , para. 1 100, 27 Patients, controls 1124 group: P = 3 × 10-22, RSN: P = 4 × 10−7, FD: P = 0.26 1 Unpaired t test Group homotopic FC difference Results: Abnormal FC Patterns in Stroke , para. 2 100, 27 Patients, controls df = 124 P = 0.000040 t = 4.26 1 Unpaired t test Group ipsilesional FC difference Results: Abnormal FC Patterns in Stroke , para. 2 100, 27 Patients, controls df = 124 P = 0.26 t = 1.13 1 Unpaired t test Group contralesional FC difference Results: Abnormal FC Patterns in Stroke , para. 2 100, 27 Patients, controls df = 124 P = 0.32 t = 1.00 1 Unpaired t test Group ipsi DAN-DMN FC difference Results: Abnormal FC Patterns in Stroke , para. 2 100, 27 Patients, controls df = 124 P = 0.0021 t = -3.15 S1B Nonparametric permutation testing of t-stat Multiple intranetwork FC differences Results: Abnormal FC Patterns in Stroke , para. 2 27 tests - 10,000 perms Patients, controls n/a 7/27 P < 0.0017 S1B Nonparametric permutation testing of t-stat Multiple internetwork FC differences Results: Abnormal FC Patterns in Stroke , para. 3 56 tests - 10,000 perms Patients, controls n/a 1/56 P < 0.0019 1 Pearson correlation DAN homotopic FC vs. ipsi DAN-DMN FC Results: Abnormal FC Patterns in Stroke , para. 3 100 Stroke patients df = 98, P = 1 × 10−12 r = −0.61 1 Pearson correlation DAN homotopic FC vs. ipsi DAN-DMN FC Results: Abnormal FC Patterns in Stroke, para. 3 27 Controls df = 25, P = 0.19 r = 0.25 1 Fisher r-to-z comparison DAN homotopic FC vs. ipsi DAN-DMN FC, different in patients Results: Abnormal FC Patterns in Stroke, para. 3 100, 27 Patients, controls df = 124, P = 0 z = −4.23 — Pearson correlation Lesion size vs. homotopic FC Results: Abnormal FC Patterns in Stroke, para. 4 100 Stroke patients df = 98, P = 6 × 10−7 r = 0.46 — Pearson correlation Lesion predicted vs. actual homotopic FC Results: Abnormal FC Patterns in Stroke, para. 4 100 Stroke patients df = 98, P = 7 × 10−7 r = 0.46 3 Permutation testing Attention Lesion-deficit model Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 80 Stroke patients r2 = 0.323 P = 4 × 10−4 3 Permutation testing Attention FC-deficit model Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 80 Stroke patients r2 = 0.450 P < 1 × 10−4 3 Permutation testing Visual mem lesion-deficit model Results Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 78 Stroke patients r2 = 0.109 P = 9 × 10−4 3 Permutation testing Visual mem FC-deficit model Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 78 Stroke patients r2 = 0.364 P < 1 × 10−4 3 Permutation testing Verbal mem Lesion-deficit model Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 79 Stroke patients r2 = 0.187 P = 1.5 × 10−3 3 Permutation testing Verbal mem FC-deficit model Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 79 Stroke patients r2 = 0.416 P < 1 × 10−4 3 Permutation testing Language lesion-deficit model Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 98 Stroke patients r2 = 0.646 P < 1 × 10−4 3 Permutation testing Language FC-deficit model Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 98 Stroke patients r2 = 0.511 P < 1 × 10−4 3 Permutation testing L motor lesion-deficit model Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 91 Stroke patients r2 = 0.469 P < 1 × 10−4 S6 Permutation testing L motor FC-deficit model Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 91 Stroke patients r2 = 0.136 P = 1.1 × 10−3 S6 Permutation testing R motor lesion-deficit model Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 91 Stroke patients r2 = 0.338 P < 1 × 10−4 S6 Permutation testing R motor FC-deficit model Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 91 Stroke patients r2 = 0.325 P < 1 × 10−4 S6 Permutation testing L visual lesion-deficit model Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 53 Stroke patients r2 = 0.331 P = 4 × 10−4 S6 Permutation testing L visual FC-deficit model Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 53 Stroke patients r2 = 0.110 P = 0.0104 S6 Permutation testing R visual lesion-deficit model Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 53 Stroke patients r2 = 0.603 P = 1 × 10−4 S6 Permutation testing R visual FC-deficit model Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 53 Stroke patients r2 = 0.081 P = 0.0902 3 Wilcoxon paired sample signed rank test Attention FC-deficit model error vs. lesion-deficit model Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 2 80 Prediction errors SR = 1343 P = 0.074 3 Wilcoxon paired sample signed rank test Visual mem FC-deficit model error vs. lesion-deficit model error Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 3 78 Prediction errors SR = 1748 P = 0.015 3 Wilcoxon paired sample signed rank test Verbal mem FC-deficit model error vs. lesion-deficit model error Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 3 79 Prediction errors SR = 2062 P = 0.007 3 Wilcoxon paired sample signed rank test Language FC-deficit model error vs. lesion-deficit model error Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 3 98 Prediction errors SR = 2205 P = 0.21 3 Wilcoxon paired sample signed rank test Motor FC-deficit model error vs. lesion-deficit model error Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 3 91 Prediction errors SR = 1501 P = 0.009 3 Wilcoxon paired sample signed rank test Visual FC-deficit model error vs. lesion-deficit model error Results: Prediction of Behavioral Deficits Based on Lesion and FC, para. 3 53 Prediction errors SR = 466 P = 0.013 4 ANOVA Connection weights (+/− within/inter hem) Results: Topography of Behaviorally Predictive FC, para. 2 8 FC-deficit models df = 31 P = 1.6 × 10−6 F = 38.03 6 Pearson correlation Multidomain FC-deficit model Results: Prediction of Common Behavioral Impairment, para. 1 623 Patients x domains r2 = 0.287 P < 1 × 10−30 S2 Pearson correlation Homotopic FC vs. framewise displacement Results: Abnormal FC Patterns in Stroke, para. 5 100 Stroke patients r = −0.15 P = 0.13 S2 Pearson correlation Homotopic FC vs. eyes open Results: Abnormal FC Patterns in Stroke, para. 5 100 Stroke patients r = 0.08 P = 0.5 S2 Pearson correlation Homotopic FC vs. hemodynamic lag Results: Abnormal FC Patterns in Stroke, para. 5 100 Stroke patients r = −0.15 P = 0.13
• Note that prediction of raw performance measures in Fig. S5 is not included here because prediction accuracy was assessed qualitatively and statistical significance tests are not reported. df, degrees of freedom; F, F-statistic; r, Pearson’s rho; SR, signed rank; t, t-statistic; z, z-statistic.