Spatial independent component analysis (ICA) applied to functional magnetic resonance imaging (fMRI) data identifies functionally connected networks by estimating spatially independent patterns from their linearly mixed fMRI signals. Several multi-subject ICA approaches estimating subject-specific time courses (TCs) and spatial maps (SMs) have been developed, however there has not yet been a full comparison of the implications of their use. Here, we provide extensive comparisons of four multi-subject ICA approaches in combination with data reduction methods for simulated and fMRI task data.For multi-subject ICA, the data first undergo reduction at the subject and group levels using principal component analysis (PCA). Comparisons of subject-specific, spatial concatenation, and group data mean subject-level reduction strategies using PCA and probabilistic PCA (PPCA) show that computationally intensive PPCA is equivalent to PCA, and that subject-specific and group data mean subject-level PCA are preferred because of well-estimated TCs and SMs. Second, aggregate independent components are estimated using either noise free ICA or probabilistic ICA (PICA). Third, subject-specific SMs and TCs are estimated using backreconstruction. We compare several direct group ICA (GICA) back-reconstruction approaches (GICA1-GICA3) and an indirect back-reconstruction approach, spatio-temporal regression (STR, or dual regression). Results show the earlier group ICA (GICA1) approximates STR, however STR has contradictory assumptions and may show mixed-component artifacts in estimated SMs. Our evidence-based recommendation is to use GICA3, introduced here, with subject-specific PCA and noise-free ICA, providing the most robust and accurate estimated SMs and TCs in addition to offering an intuitive interpretation.

To resolve the genetic heterogeneity within pediatric high-risk B-precursor acute lymphoblastic leukemia (ALL), a clinically defined poor-risk group with few known recurring cytogenetic abnormalities, we performed gene expression profiling in a cohort of 207 uniformly treated children with high-risk ALL. Expression profiles were correlated with genome-wide DNA copy number abnormalities and clinical and outcome features. Unsupervised clustering of gene expression profiling data revealed 8 unique cluster groups within these highrisk ALL patients, 2 of which were associated with known chromosomal translocations (t(1;19)(TCF3-PBX1) or MLL), and 6 of which lacked any previously known cytogenetic lesion. One unique cluster was characterized by high expression of distinct outlier genes AGAP1, CCNJ, CHST2/7, CLEC12A/B, and PTPRM; ERG DNA deletions; and 4-year relapse-free survival of 94.7% ؎ 5.1%, compared with 63.5% ؎ 3.7% for the cohort (P ‫؍‬ .01). A second cluster, characterized by high expression of BMPR1B, CRLF2, GPR110, and MUC4; frequent deletion of EBF1, IKZF1, RAG1-2, and IL3RA-CSF2RA; JAK mutations and CRLF2 rearrangements (P < .0001); and Hispanic ethnicity (P < .001) had a very poor 4-year relapsefree survival (21.0% ؎ 9.5%; P < .001). These studies reveal striking clinical and genetic heterogeneity in high-risk ALL and point to novel genes that may serve as new targets for diagnosis, risk classification, and therapy. (Blood. 2010; 116(23):4874-4884)

Intravenous vitamin D is standard therapy for secondary hyperparathyroidism in hemodialysis (HD) patients. In for-profit dialysis clinics, mortality was higher for patients on calcitriol compared to paricalcitol. Doxercalciferol, a second vitamin D2 analog, is currently available. We assessed mortality associated with each vitamin D analog and with lack of vitamin D therapy in patients who began HD at Dialysis Clinic Inc. (DCI), a not-for-profit dialysis provider. During the 1999-2004 study period we studied 7731 patients (calcitriol: n=3212; paricalcitol: n=2087; doxercalciferol: n=2432). Median follow-up was 37 weeks. Mortality rates (deaths/100 patient-years) were identical in patients on doxercalciferol (15.4, 95% confidence interval (13.6-17.1)) and paricalcitol (15.3 (13.6-16.9)) and higher in patients on calcitriol (19.6 (18.2-21.1)) (P<0.0001). In all models mortality was similar for paricalcitol versus doxercalciferol (hazard ratios=1.0). In unadjusted models, mortality was lower in patients on doxercalciferol (0.80 (0.66, 0.96)) and paricalcitol (0.79 (0.68, 0.92)) versus calcitriol (P<0.05). In adjusted models, this difference was not statistically significant. In all models mortality was higher for patients who did not receive vitamin D versus those who did (1.2 (1.1-1.3)). Mortality in doxercalciferol- and paricalcitol-treated patients was virtually identical. Differences in survival between vitamin D2 and D3 may be smaller than previously reported.

To determine whether gene expression profiling could improve outcome prediction in children with acute lymphoblastic leukemia (ALL) at high risk for relapse, we profiled pretreatment leukemic cells in 207 uniformly treated children with highrisk B-precursor ALL. A 38-gene expression classifier predictive of relapse-free survival (RFS) could distinguish 2 groups with differing relapse risks: low (4-year RFS, 81%, n ‫؍‬ 109) versus high (4-year RFS, 50%, n ‫؍‬ 98; P < .001). In multivariate analysis, the gene expression classifier (P ‫؍‬ .001) and flow cytometric measures of minimal residual disease (MRD; P ‫؍‬ .001) each provided independent prognostic information. Together, they could be used to classify children with high-risk ALL into low-(87% RFS), intermediate-(62% RFS), or high-(29% RFS) risk groups (P < .001). A 21-gene expression classifier predictive of end-induction MRD effectively substituted for flow MRD, yielding a combined classifier that could distinguish these 3 risk groups at diagnosis (P < .001). These classifiers were further validated on an independent highrisk ALL cohort (P ‫؍‬ .006) and retained independent prognostic significance (P < .001) in the presence of other recently described poor prognostic factors (IKAROS/IKZF1 deletions, JAK mutations, and kinase expression signatures