@article {684,
	title = {Corrected confidence bands for functional data using principal components.},
	journal = {Biometrics},
	volume = {69},
	year = {2013},
	month = {2013 Mar},
	pages = {41-51},
	abstract = {<p>Functional principal components (FPC) analysis is widely used to decompose and express functional observations. Curve estimates implicitly condition on basis functions and other quantities derived from FPC decompositions; however these objects are unknown in practice. In this article, we propose a method for obtaining correct curve estimates by accounting for uncertainty in FPC decompositions. Additionally, pointwise and simultaneous confidence intervals that account for both model- and decomposition-based variability are constructed. Standard mixed model representations of functional expansions are used to construct curve estimates and variances conditional on a specific decomposition. Iterated expectation and variance formulas combine model-based conditional estimates across the distribution of decompositions. A bootstrap procedure is implemented to understand the uncertainty in principal component decomposition quantities. Our method compares favorably to competing approaches in simulation studies that include both densely and sparsely observed functions. We apply our method to sparse observations of CD4 cell counts and to dense white-matter tract profiles. Code for the analyses and simulations is publicly available, and our method is implemented in the R package refund on CRAN.</p>},
	keywords = {Brain, CD4 Lymphocyte Count, Computer Simulation, Confidence Intervals, HIV, HIV Infections, Humans, Magnetic Resonance Imaging, Models, Statistical, Multiple Sclerosis, Principal Component Analysis},
	issn = {1541-0420},
	doi = {10.1111/j.1541-0420.2012.01808.x},
	author = {Goldsmith, J and Greven, S and Crainiceanu, C}
}
@article {622,
	title = {Dynamics of large-scale cortical interactions at high gamma frequencies during word production: event related causality (ERC) analysis of human electrocorticography (ECoG).},
	journal = {Neuroimage},
	volume = {56},
	year = {2011},
	month = {2011 Jun 15},
	pages = {2218-37},
	abstract = {<p>Intracranial EEG studies in humans have shown that functional brain activation in a variety of functional-anatomic domains of human cortex is associated with an increase in power at a broad range of high gamma (\&gt;60Hz) frequencies. Although these electrophysiological responses are highly specific for the location and timing of cortical processing and in animal recordings are highly correlated with increased population firing rates, there has been little direct empirical evidence for causal interactions between different recording sites at high gamma frequencies. Such causal interactions are hypothesized to occur during cognitive tasks that activate multiple brain regions. To determine whether such causal interactions occur at high gamma frequencies and to investigate their functional significance, we used event-related causality (ERC) analysis to estimate the dynamics, directionality, and magnitude of event-related causal interactions using subdural electrocorticography (ECoG) recorded during two word production tasks: picture naming and auditory word repetition. A clinical subject who had normal hearing but was skilled in American Signed Language (ASL) provided a unique opportunity to test our hypothesis with reference to a predictable pattern of causal interactions, i.e. that language cortex interacts with different areas of sensorimotor cortex during spoken vs. signed responses. Our ERC analyses confirmed this prediction. During word production with spoken responses, perisylvian language sites had prominent causal interactions with mouth/tongue areas of motor cortex, and when responses were gestured in sign language, the most prominent interactions involved hand and arm areas of motor cortex. Furthermore, we found that the sites from which the most numerous and prominent causal interactions originated, i.e. sites with a pattern of ERC "divergence", were also sites where high gamma power increases were most prominent and where electrocortical stimulation mapping interfered with word production. These findings suggest that the number, strength and directionality of event-related causal interactions may help identify network nodes that are not only activated by a task but are critical to its performance.</p>},
	keywords = {brain mapping, Cerebral Cortex, Electroencephalography, Female, Humans, Middle Aged, Neural Pathways, Seizures, Signal Processing, Computer-Assisted, Speech},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2011.03.030},
	author = {Korzeniewska, A. and Franaszczuk, Piotr J. and Crainiceanu, C and Ku{\'s}, Rafa{\l} and Crone, Nathan E.}
}
