Deborah Yurgelun-Todd

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Synchronized low-frequency BOLD fluctuations are observed in dissociable large-scale, distributed networks with functional specialization. Two such networks, referred to as the task-positive network (TPN) and the task-negative network (TNN) because they tend to be active or inactive during cognitively demanding tasks, show reproducible anticorrelation of(More)
BACKGROUND AND PURPOSE Measurements of resting-state functional connectivity have increasingly been used for characterization of neuropathologic and neurodevelopmental populations. We collected data to characterize how much imaging time is necessary to obtain reproducible quantitative functional connectivity measurements needed for a reliable single-subject(More)
Functional imaging studies have shown reduced activity within the default mode network during attention-demanding tasks. The network circuitry underlying this suppression remains unclear. Proposed hypotheses include an attentional switch in the right anterior insula and reciprocal inhibition between the default mode and attention control networks. We(More)
BACKGROUND AND PURPOSE Deep brain stimulation of the thalamus has become a valuable treatment for medication-refractory essential tremor, but current targeting provides only a limited ability to account for individual anatomic variability. We examined whether functional connectivity measurements among the motor cortex, superior cerebellum, and thalamus(More)
The intraparietal sulcus (IPS) region is uniquely situated at the intersection of visual, somatosensory, and auditory association cortices, ideally located for processing of multisensory attention. We examined the internal architecture of the IPS region and its connectivity to other regions in the dorsal attention and cinguloinsular networks using maximal(More)
We describe an efficient algorithm for the step-down permutation test, applied to the analysis of functional magnetic resonance images. The algorithm's time bound is nearly linear, making it feasible as an interactive tool. Results of the permutation test algorithm applied to data from a cognitive activation paradigm are compared with those of a standard(More)
In this paper we present a new method for spatial regularization of functional connectivity maps based on Markov Random Field (MRF) priors. The high level of noise in fMRI leads to errors in functional connectivity detection algorithms. A common approach to mitigate the effects of noise is to apply spatial Gaussian smoothing, which can lead to blurring of(More)
An initial course in disentangling complex causal interactions in psychiatric illnesses, we suggest, is finding co-familial traits with classical Mendelian segregation. Starting with non-Mendelian traits, three methods can be used to find underlying Mendelian phenotypes. (1) Statistically-inferred latent traits, with more nearly Mendelian transmission than(More)
We propose a novel Bayesian framework for partitioning the cortex into distinct functional networks based on resting-state fMRI. Spatial coherence within the network clusters is modeled using a hidden Markov random field prior. The normalized time-series data, which lie on a high-dimensional sphere, are modeled with a mixture of von Mises-Fisher(More)
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