Human immunodeficiency virus (HIV-1) variants in brain primarily use CCR5 for entry into macrophages and microglia, but dual-tropic (R5X4) HIV-1 has been detected in brain and cerebral spinal fluid (CSF) of some patients with HIV-associated dementia (HAD). Here, we sequenced the gp120 coding region of nine full-length dual-tropic (R5X4) env genes cloned directly from autopsy brain and spleen tissue from an AIDS patient with severe HAD. We then compiled a dataset of 30 unique clade B R5X4 Env V3 sequences from this subject and 16 additional patients (n = 4 brain and 26 lymphoid/blood) and used it to compare the ability of six bioinformatic algorithms to correctly predict CXCR4 usage in R5X4 Envs. Only one program (SVM(geno2pheno)) correctly predicted the ability of R5X4 Envs in this dataset to use CXCR4 with 90% accuracy (n = 27/30 predicted to use CXCR4). The PSSM(SINSI), Random Forest, and SVM(genomiac) programs and the commonly used charge rule correctly predicted CXCR4 usage with >50% accuracy (22/30, 16/30, 19/30, and 25/30, respectively), while the PSSM(X4R5) matrix and "11/25" rule correctly predicted CXCR4 usage in <50% of the R5X4 Envs (10/30 and 13/30, respectively). Two positions in the V3 loop (19 and 32) influenced coreceptor usage predictions of nine R5X4 Envs from patient MACS1 and a total of 12 Envs from the dataset (40% of unique V3 sequences). These results demonstrate that most predictive algorithms underestimate the frequency of R5X4 HIV-1 in brain and other tissues. SVM(geno2pheno) is the most accurate predictor of CXCR4 usage by R5X4 HIV-1.