@article {794, title = {Estimating energy expenditure from heart rate in older adults: a case for calibration.}, journal = {PLoS One}, volume = {9}, year = {2014}, month = {2014}, pages = {e93520}, abstract = {

BACKGROUND: Accurate measurement of free-living energy expenditure is vital to understanding changes in energy metabolism with aging. The efficacy of heart rate as a surrogate for energy expenditure is rooted in the assumption of a linear function between heart rate and energy expenditure, but its validity and reliability in older adults remains unclear.

OBJECTIVE: To assess the validity and reliability of the linear function between heart rate and energy expenditure in older adults using different levels of calibration.

DESIGN: Heart rate and energy expenditure were assessed across five levels of exertion in 290 adults participating in the Baltimore Longitudinal Study of Aging. Correlation and random effects regression analyses assessed the linearity of the relationship between heart rate and energy expenditure and cross-validation models assessed predictive performance.

RESULTS: Heart rate and energy expenditure were highly correlated (r=0.98) and linear regardless of age or sex. Intra-person variability was low but inter-person variability was high, with substantial heterogeneity of the random intercept (s.d. =0.372) despite similar slopes. Cross-validation models indicated individual calibration data substantially improves accuracy predictions of energy expenditure from heart rate, reducing the potential for considerable measurement bias. Although using five calibration measures provided the greatest reduction in the standard deviation of prediction errors (1.08 kcals/min), substantial improvement was also noted with two (0.75 kcals/min).

CONCLUSION: These findings indicate standard regression equations may be used to make population-level inferences when estimating energy expenditure from heart rate in older adults but caution should be exercised when making inferences at the individual level without proper calibration.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0093520}, author = {Schrack, Jennifer A and Zipunnikov, Vadim and Goldsmith, Jeff and Bandeen-Roche, Karen and Crainiceanu, Ciprian M and Ferrucci, Luigi} } @article {835, title = {Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach.}, journal = {Neuroimage}, volume = {101}, year = {2014}, month = {2014 Nov 1}, pages = {531-46}, abstract = {

To date, most functional Magnetic Resonance Imaging (fMRI) studies have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant across time. However, recently, there has been an increased interest in quantifying possible dynamic changes in FC during fMRI experiments, as it is thought that this may provide insight into the fundamental workings of brain networks. In this work we focus on the specific problem of estimating the dynamic behavior of pair-wise correlations between time courses extracted from two different regions of the brain. We critique the commonly used sliding-window technique, and discuss some alternative methods used to model volatility in the finance literature that could also prove to be useful in the neuroimaging setting. In particular, we focus on the Dynamic Conditional Correlation (DCC) model, which provides a model-based approach towards estimating dynamic correlations. We investigate the properties of several techniques in a series of simulation studies and find that DCC achieves the best overall balance between sensitivity and specificity in detecting dynamic changes in correlations. We also investigate its scalability beyond the bivariate case to demonstrate its utility for studying dynamic correlations between more than two brain regions. Finally, we illustrate its performance in an application to test-retest resting state fMRI data.

}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2014.06.052}, author = {Lindquist, Martin A and Xu, Yuting and Nebel, Mary Beth and Caffo, Brain S} } @article {822, title = {An evaluation of independent component analyses with an application to resting-state fMRI.}, journal = {Biometrics}, volume = {70}, year = {2014}, month = {2014 Mar}, pages = {224-36}, abstract = {

We examine differences between independent component analyses (ICAs) arising from different assumptions, measures of dependence, and starting points of the algorithms. ICA is a popular method with diverse applications including artifact removal in electrophysiology data, feature extraction in microarray data, and identifying brain networks in functional magnetic resonance imaging (fMRI). ICA can be viewed as a generalization of principal component analysis (PCA) that takes into account higher-order cross-correlations. Whereas the PCA solution is unique, there are many ICA methods-whose solutions may differ. Infomax, FastICA, and JADE are commonly applied to fMRI studies, with FastICA being arguably the most popular. Hastie and Tibshirani (2003) demonstrated that ProDenICA outperformed FastICA in simulations with two components. We introduce the application of ProDenICA to simulations with more components and to fMRI data. ProDenICA was more accurate in simulations, and we identified differences between biologically meaningful ICs from ProDenICA versus other methods in the fMRI analysis. ICA methods require nonconvex optimization, yet current practices do not recognize the importance of, nor adequately address sensitivity to, initial values. We found that local optima led to dramatically different estimates in both simulations and group ICA of fMRI, and we provide evidence that the global optimum from ProDenICA is the best estimate. We applied a modification of the Hungarian (Kuhn-Munkres) algorithm to match ICs from multiple estimates, thereby gaining novel insights into how brain networks vary in their sensitivity to initial values and ICA method.

}, keywords = {Adolescent, Algorithms, Attention Deficit Disorder with Hyperactivity, brain mapping, Child, Child, Preschool, Computer Simulation, Humans, Magnetic Resonance Imaging, Models, Statistical, Principal Component Analysis}, issn = {1541-0420}, doi = {10.1111/biom.12111}, author = {Risk, Benjamin B and Matteson, David S and Ruppert, David and Eloyan, Ani and Caffo, Brian S} } @article {816, title = {Evidence for Specificity of Motor Impairments in Catching and Balance in Children with Autism.}, journal = {J Autism Dev Disord}, year = {2014}, month = {2014 Sep 18}, abstract = {

To evaluate evidence for motor impairment specificity in autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). Children completed performance-based assessment of motor functioning (Movement Assessment Battery for Children: MABC-2). Logistic regression models were used to predict group membership. In the models comparing typically developing and developmental disability (DD), all three MABC subscale scores were significantly negatively associated with having a DD. In the models comparing ADHD and ASD, catching and static balance items were associated with ASD group membership, with a 1 point decrease in performance increasing odds of ASD by 36 and 39~\%, respectively. Impairments in motor skills requiring the coupling of visual and temporal feedback to guide and adjust movement appear specifically deficient in ASD.

}, issn = {1573-3432}, doi = {10.1007/s10803-014-2229-0}, author = {Ament, Katarina and Mejia, Amanda and Buhlman, Rebecca and Erklin, Shannon and Caffo, Brian and Mostofsky, Stewart and Wodka, Ericka} } @article {826, title = {Effects of working memory demand on neural mechanisms of motor response selection and control.}, journal = {J Cogn Neurosci}, volume = {25}, year = {2013}, month = {2013 Aug}, pages = {1235-48}, abstract = {

Inhibitory control commonly recruits a number of frontal regions: pre-supplementary motor area (pre-SMA), frontal eye fields (FEFs), and right-lateralized posterior inferior frontal gyrus (IFG), dorsal anterior insula (DAI), dorsolateral prefrontal cortex (DLPFC), and inferior frontal junction (IFJ). These regions may directly implement inhibitory motor control or may be more generally involved in executive control functions. Two go/no-go tasks were used to distinguish regions specifically recruited for inhibition from those that additionally show increased activity with working memory demand. The pre-SMA and IFG were recruited for inhibition in both tasks and did not have greater activation for working memory demand on no-go trials, consistent with a role in inhibitory control. Activation in pre-SMA also responded to response selection demand and was increased with working memory on go trials specifically. The bilateral FEF and right DAI were commonly active for no-go trials. The FEF was also recruited to a greater degree with working memory demand on go trials and may bias top-down information when stimulus-response mappings change. The DAI, additionally responded to increased working memory demand on both go and no-go trials and may be involved in accessing sustained task information, alerting, or autonomic changes when cognitive demands increase. DLPFC activation was consistent with a role in working memory retrieval on both go and no-go trials. The inferior frontal junction, on the other hand, had greater activation with working memory specifically for no-go trials and may detect salient stimuli when the task requires frequent updating of working memory representations.

}, keywords = {Adult, Brain, brain mapping, Choice Behavior, Female, Humans, Image Processing, Computer-Assisted, Inhibition (Psychology), Magnetic Resonance Imaging, Male, Memory, Short-Term, Motor Activity, Neuropsychological Tests, Oxygen, Reaction Time, Young Adult}, issn = {1530-8898}, doi = {10.1162/jocn_a_00394}, author = {Barber, Anita D and Caffo, Brian S and Pekar, James J and Mostofsky, Stewart H} } @article {pmid22221585, title = {Effect of correcting for long-term variation in major coronary heart disease risk factors: relative hazard estimation and risk prediction in the Atherosclerosis Risk in Communities Study}, journal = {Ann Epidemiol}, volume = {22}, number = {3}, year = {2012}, month = {Mar}, pages = {191{\textendash}197}, author = {Paynter, N. P. and Crainiceanu, C and Sharrett, A. R. and Chambless, L. E. and Coresh, J.} } @article {844, title = {Estimating and testing variance components in a multi-level GLM.}, journal = {Neuroimage}, volume = {59}, year = {2012}, month = {2012 Jan 2}, pages = {490-501}, abstract = {

Most analysis of multi-subject fMRI data is concerned with determining whether there exists a significant population-wide {\textquoteright}activation{\textquoteright} in a comparison between two or more conditions. This is typically assessed by testing the average value of a contrast of parameter estimates (COPE) against zero in a general linear model (GLM) analysis. However, important information can also be obtained by testing whether there exist significant individual differences in effect magnitude between subjects, i.e. whether the variance of a COPE is significantly different from zero. Intuitively, such a test amounts to testing whether inter-individual differences are larger than would be expected given the within-subject error variance. We compare several methods for estimating variance components, including a) a na{\"\i}ve estimate using ordinary least squares (OLS); b) linear mixed effects in R (LMER); c) a novel Matlab implementation of iterative generalized least squares (IGLS) and its restricted maximum likelihood variant (RIGLS). All methods produced reasonable estimates of within- and between-subject variance components, with IGLS providing an attractive balance between sensitivity and appropriate control of false positives. Finally, we use the IGLS method to estimate inter-subject variance in a perfusion fMRI study (N=18) of social evaluative threat, and show evidence for significant inter-individual differences in ventromedial prefrontal cortex (VMPFC), amygdala, hippocampus and medial temporal lobes, insula, and brainstem, with predicted inverse coupling between VMPFC and the midbrain periaqueductal gray only when high inter-individual variance was used to define the seed for functional connectivity analyses. In sum, tests of variance provides a way of selecting regions that show significant inter-individual variability for subsequent analyses that attempt to explain those individual differences.

}, keywords = {Brain, brain mapping, Humans, Image Interpretation, Computer-Assisted, Linear Models, Magnetic Resonance Imaging, Models, Neurological, Sensitivity and Specificity}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2011.07.077}, author = {Lindquist, Martin A and Spicer, Julie and Asllani, Iris and Wager, Tor D} } @article {lindquist2012estimating, title = {Estimating and testing variance components in a multi-level GLM}, journal = {Neuroimage}, volume = {59}, number = {1}, year = {2012}, pages = {490{\textendash}501}, publisher = {Elsevier}, author = {Lindquist, Martin A and Spicer, Julie and Asllani, Iris and Wager, Tor D} } @article {reiss2011extracting, title = {Extracting information from functional connectivity maps via function-on-scalar regression}, journal = {NeuroImage}, volume = {56}, number = {1}, year = {2011}, pages = {140{\textendash}148}, author = {Reiss, P.T. and Mennes, M. and Petkova, E. and Huang, L. and Hoptman, M.J. and Biswal, B.B. and Colcombe, S.J. and Zuo, X.N. and Milham, M.P.} } @article {staicu2010, title = {On the equivalence of the prospective and retrospective likelihood methods in casecontrol studies}, journal = {Biometrika}, volume = {97}, year = {2010}, pages = {990{\textendash}996}, author = {Staicu, A. M.} } @article {schwartz2010evaluation, title = {Evaluation of Cumulative Lead Dose and Longitudinal Changes in Structural Magnetic Resonance Imaging in Former Organolead Workers}, journal = {Journal of Occupational and Environmental Medicine, PMCID: PMC2869464}, volume = {52}, number = {4}, year = {2010}, pages = {407{\textendash}414}, author = {Schwartz, B and Caffo, B and Stewart, W and Hedlin, H and James, B and Yousem, D and Davatzikos, C.} } @article {847, title = {Everything you never wanted to know about circular analysis, but were afraid to ask.}, journal = {J Cereb Blood Flow Metab}, volume = {30}, year = {2010}, month = {2010 Sep}, pages = {1551-7}, abstract = {

Over the past year, a heated discussion about {\textquoteright}circular{\textquoteright} or {\textquoteright}nonindependent{\textquoteright} analysis in brain imaging has emerged in the literature. An analysis is circular (or nonindependent) if it is based on data that were selected for showing the effect of interest or a related effect. The authors of this paper are researchers who have contributed to the discussion and span a range of viewpoints. To clarify points of agreement and disagreement in the community, we collaboratively assembled a series of questions on circularity herein, to which we provide our individual current answers in }, keywords = {Brain, brain mapping, Data Interpretation, Statistical, Guidelines as Topic, Image Processing, Computer-Assisted, Neurosciences, Publications, Research Design, Selection Bias}, issn = {1559-7016}, doi = {10.1038/jcbfm.2010.86}, author = {Kriegeskorte, Nikolaus and Lindquist, Martin A and Nichols, Thomas E and Poldrack, Russell A and Vul, Edward} } @article {kriegeskorte2010everything, title = {Everything you never wanted to know about circular analysis, but were afraid to ask}, journal = {Journal of Cerebral Blood Flow \& Metabolism}, volume = {30}, number = {9}, year = {2010}, pages = {1551{\textendash}1557}, publisher = {Nature Publishing Group}, author = {Kriegeskorte, Nikolaus and Lindquist, Martin A and Nichols, Thomas E and Poldrack, Russell A and Vul, Edward} } @article {lauzon2009easy, title = {Easy Multiplicity Control in Equivalence Testing Using Two One-Sided Tests}, journal = {The American Statistician, PMCID: PMC2800314}, volume = {63}, number = {2}, year = {2009}, pages = {147{\textendash}154}, publisher = {ASA}, author = {Lauzon, C and Caffo, B} } @article {wager2009evaluating, title = {Evaluating the consistency and specificity of neuroimaging data using meta-analysis}, journal = {Neuroimage}, volume = {45}, number = {1}, year = {2009}, pages = {S210{\textendash}S221}, publisher = {Elsevier}, author = {Wager, Tor D and Lindquist, Martin A and Nichols, Thomas E and Kober, Hedy and Van Snellenberg, Jared X} } @article {cao2007effect, title = {Effect of semen sampling frequency on seminal antiretroviral drug concentration}, journal = {Clinical Pharmacology \& Therapeutics}, volume = {83}, number = {6}, year = {2007}, pages = {848{\textendash}856}, publisher = {Nature Publishing Group}, author = {Cao, Y and Ndovi, T and Parsons, T and Guidos, A and Caffo, B and Hendrix, C} } @article {caffo2006exact, title = {Exact Hypothesis Tests for Log-linear Models with exactLoglinTest}, journal = {Journal of Statistical Software}, volume = {17}, number = {7}, year = {2006}, pages = {1{\textendash}17}, author = {Caffo, B} } @article {pmid15817517, title = {Electrocorticographic high gamma activity versus electrical cortical stimulation mapping of naming}, journal = {Brain}, volume = {128}, number = {Pt 7}, year = {2005}, month = {Jul}, pages = {1556{\textendash}1570}, author = {Sinai, A. and Bowers, C. W. and Crainiceanu, C and Boatman, D. and Gordon, B. and Lesser, R. P. and Lenz, F. A. and Crone, N.E.} } @article {Crai:Rupp:Clae:Wand:exac:2005, title = {Exact Likelihood Ratio Tests for Penalised Splines}, journal = {Biometrika}, volume = {92}, number = {1}, year = {2005}, pages = {91{\textendash}103}, keywords = {Linear mixed model, Penalised spline, Smoothing, Zero variance component}, author = {Crainiceanu, C and Ruppert, D. and Claeskens, Gerda and Wand, M. P.} } @article {agresti2004examples, title = {Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies}, journal = {Computational Statistics and Data Analysis}, volume = {47}, number = {3}, year = {2004}, pages = {639{\textendash}653}, publisher = {Elsevier}, author = {Agresti, A and Caffo, B and Ohman-Strickland, P} } @article {caffo2002empirical, title = {Empirical supremum rejection sampling}, journal = {Biometrika}, volume = {89}, number = {4}, year = {2002}, pages = {745{\textendash}754}, publisher = {Biometrika Trust}, author = {Caffo, B and Booth, J and Davison, A} }