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. 2017 Jul 11;28(8):2922–2934. doi: 10.1093/cercor/bhx170

Table 1.

Algorithm performance by network

Networks nodes DS 1 DS 2
GA SA GA SA
Acci–f Cfif Rn Accif Cfif Rn Accif Cfif Rn Accif Cfif Rn
DMN-418 30–30 0.28–0.62 400 30–30 0.28–0.62 401 ± 0 39–39 0.27–0.27 0 39–39 0.27–0.27 0
FP-192 30–30 0.32–0.39 72 30–30 0.32–0.62 181 ± 1.7 39–40 0.32–0.45 61 39–40 0.32–0.55 176 ± 3.4
ORB-185 22–22 0.22–0.23 9 22–22 0.22–0.23 85 ± 9.2 27–28 0.17–0.17 4 27–31 0.17–0.37 171 ± 3
SAL-124 28–28 0.27–0.27 0 30–30 0.27–0.51 111 ± 1.5 38–38 0.27–0.27 0 38–38 0.27–0.27 0
DAN-76 29–29 0.28–0.28 0 29–29 0.28–0.48 63 ± 2 35–37 0.27–0.34 29 35–37 0.27–0.34 52 ± 4
ASM-394 24–24 0.21–0.22 9 24–24 0.21–0.34 347 ± 4 31–33 0.23–0.26 90 31–34 0.23–0.44 381 ± 2.8
VIS-350 25–25 0.19–0.21 28 25–25 0.19–0.25 140 ± 10 30–30 0.17–0.17 0 30–30 0.17–0.17 0

Acc: Accuracy, Cf: Cost function, Rn: Removed nodes. Subindices i and f denote the values before and after running the algorithm, respectively. Values (mean ± std) from the SA algorithm were obtained after averaging across multiple runs (20). Names and total number of nodes comprising each network are listed on the leftmost column. See text for abbreviations of brain networks.