@article {812, title = {Likelihood-based population independent component analysis.}, journal = {Biostatistics}, volume = {14}, year = {2013}, month = {2013 Jul}, pages = {514-27}, abstract = {

Independent component analysis (ICA) is a widely used technique for blind source separation, used heavily in several scientific research areas including acoustics, electrophysiology, and functional neuroimaging. We propose a scalable two-stage iterative true group ICA methodology for analyzing population level functional magnetic resonance imaging (fMRI) data where the number of subjects is very large. The method is based on likelihood estimators of the underlying source densities and the mixing matrix. As opposed to many commonly used group ICA algorithms, the proposed method does not require significant data reduction by a 2-fold singular value decomposition. In addition, the method can be applied to a large group of subjects since the memory requirements are not restrictive. The performance of our approach is compared with a commonly used group ICA algorithm via simulation studies. Furthermore, the proposed method is applied to a large collection of resting state fMRI datasets. The results show that established brain networks are well recovered by the proposed algorithm.

}, keywords = {Adult, Algorithms, Attention Deficit Disorder with Hyperactivity, Biostatistics, Brain, Child, Computer Simulation, Databases, Factual, Humans, Imaging, Three-Dimensional, Likelihood Functions, Magnetic Resonance Imaging, Principal Component Analysis, Statistics, Nonparametric}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxs055}, author = {Eloyan, Ani and Crainiceanu, Ciprian M and Caffo, Brian S} } @article {814, title = {Longitudinal scalar-on-functions regression with application to tractography data.}, journal = {Biostatistics}, volume = {14}, year = {2013}, month = {2013 Jul}, pages = {447-61}, abstract = {

We propose a class of estimation techniques for scalar-on-function regression where both outcomes and functional predictors may be observed at multiple visits. Our methods are motivated by a longitudinal brain diffusion tensor imaging tractography study. One of the study{\textquoteright}s primary goals is to evaluate the contemporaneous association between human function and brain imaging over time. The complexity of the study requires the development of methods that can simultaneously incorporate: (1) multiple functional (and scalar) regressors; (2) longitudinal outcome and predictor measurements per patient; (3) Gaussian or non-Gaussian outcomes; and (4) missing values within functional predictors. We propose two versions of a new method, longitudinal functional principal components regression (PCR). These methods extend the well-known functional PCR and allow for different effects of subject-specific trends in curves and of visit-specific deviations from that trend. The new methods are compared with existing approaches, and the most promising techniques are used for analyzing the tractography data.

}, keywords = {Anisotropy, Biostatistics, Brain, Diffusion Tensor Imaging, Humans, Linear Models, Models, Statistical, Multiple Sclerosis, Principal Component Analysis, Regression Analysis}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxs051}, author = {Gertheiss, Jan and Goldsmith, Jeff and Crainiceanu, Ciprian and Greven, Sonja} } @article {goldsmith, title = {Longitudinal penalized functional regression for cognitive outcomes on neuronal tract measurements}, journal = {Journal of the Royal Statistical Society Series C}, number = {61}, year = {2012}, pages = {453{\textendash}469}, author = {Goldsmith, J and Crainiceanu, C and Caffo, B and Reich, D.} } @article {shinohara2011longitudinal, title = {Longitudinal Analysis of Spatiotemporal Processes: A Case Study of Dynamic Contrast-Enhanced Magnetic Resonance in Multiple Sclerosis}, journal = {Johns Hopkins University, Dept. of Biostatistics Working Papers}, year = {2011}, pages = {231}, publisher = {bepress}, author = {Shinohara, R and Crainiceanu, C and Caffo, B and Reich, D S} } @article {harrison, title = {Longitudinal changes in diffusion-tensor-based quantitative MRI in multiple sclerosis}, journal = {Neurology, PMCID: PMC3030233}, volume = {76}, year = {2011}, pages = {179{\textendash}186}, author = {Harrison, D and Caffo, B and Shiee, N and Farrell, J Bazin, P and Farrell, S and Ratchford, J and Calabresi, P and Reich, D.} } @article {swihart2009lasagna, title = {Lasagna plots: A Saucy alternative to spaghetti plots}, journal = {Epidemiology}, volume = {21}, number = {5}, year = {2010}, pages = {621{\textendash}625}, author = {Swihart, B and Caffo, B and James, B and Strand, M. and Schwartz, B and Punjabi, N} } @article {greven, title = {Longitudinal functional principal components analysis}, journal = {Electronic Journal of Statistics, PMCID: PMC3131008}, volume = {4}, year = {2010}, pages = {1022-1054}, author = {Greven, S. and Crainiceanu, C and Caffo, B and Reich, D.} } @article {lindquistJASA09, title = {Logistic Regression with Brownian-like Predictors}, journal = {Journal of the American Statistical Association}, volume = {104}, year = {2009}, pages = {1575-1585}, author = {Lindquist, Martin A and McKeague, I.W.} } @article {caffo2008likelihood, title = {Likelihood Estimation of Conjugacy Relationships in Linear Models with Applications to High-Throughput Genomics}, journal = {International Journal of Biostatistics, PMCID: PMC2827886}, volume = {5}, year = {2008}, note = {Issue 18}, publisher = {bepress}, author = {Caffo, B and Dongmei, L and Scharpf, R and Parmigiani, G.} } @article {Crai:Rupp:like:2004, title = {Likelihood Ratio Tests in Linear Mixed Models with One Variance Component}, journal = {Journal of the Royal Statistical Society, Series B: Statistical Methodology}, volume = {66}, number = {1}, year = {2004}, pages = {165{\textendash}185}, keywords = {Degrees of freedom, Non-regular problems, penalized splines}, author = {Crainiceanu, C and Ruppert, D.} } @article {Greenspan1997, title = {Loss of FHIT expression in cervical carcinoma cell lines and primary tumors.}, journal = {Cancer Research}, volume = {57}, number = {21}, year = {1997}, pages = {4692{\textendash}4698}, publisher = {Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.}, abstract = {

Allelic deletions involving the short arm of chromosome 3 (3p13-21.1) have been observed frequently in cervical carcinomas. Recently, a candidate tumor suppressor gene, FHIT (Fragile Histidine Triad), was cloned and mapped to this chromosomal region (3p14.2). Abnormal FHIT transcripts have been identified previously in a variety of tumor cell lines and primary carcinomas, although their significance and the molecular mechanisms underlying their origin remain incompletely defined. In addition, integration of human papillomavirus DNA has been identified at a fragile site (FRA3B) within the FHIT locus in cervical cancer. These observations motivated us to evaluate FHIT mRNA and protein expression in cervical cancer cell lines, primary cervical carcinomas, and normal tissues. Transcripts of the expected size and sequence were the predominant species identified by reverse transcription (RT)-PCR in cultured keratinocytes and all normal tissues evaluated. In contrast, aberrant FHIT transcripts were readily demonstrated in 6 of 7 cervical carcinoma cell lines and 17 of 25 (68\%) primary cervical carcinomas. Northern blot analyses demonstrated reduced or absent FHIT expression in the cervical carcinoma cell lines, particularly those with aberrant RT-PCR products. Immunohistochemical analysis of Fhit expression in cervical tissues revealed strong immunoreactivity in nonneoplastic squamous and glandular cervical epithelium and marked reduction or loss of Fhit protein in 25 of 33 (76\%) primary cervical carcinomas. In those cervical cancer cell lines and primary tumors with exclusively aberrant or absent FHIT transcripts by RT-PCR, Fhit protein expression was always markedly reduced or absent. The frequent alterations in FHIT expression in many cervical carcinomas, but not in normal tissues, suggest that FHIT gene alterations may play an important role in cervical tumorigenesis.

}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9354423}, author = {Greenspan, D L and Connolly, D C and Wu, R and Lei, R Y and Vogelstein, Joshua T and Kim, Y T and Mok, J E and Mu{\~n}oz, N and Bosch, F X and Shah, K and Cho, K R} }