Two hypotheses were tested using a novel individual differences approach, which identifies rate-limiting brain regions, that is, brain regions in which variations in neural activity predict variations in behavioral performance. The first hypothesis is that the rate-limiting regions that support the production of lies about oneself (self-related) are partially distinct from those underlying the production of lies about other individuals (other-related).
Following adaptation to a moving stimulus, the introduction of a stationary pattern creates the illusion of motion. This phenomenon, known as the motion aftereffect (MAE), can be delayed by placing a blank storage interval between the adapting and test stimuli. Human motion selective area MT/V5 has been proposed as the likely neural origin of MAEs. To examine the role of MT/V5 in perceiving and storing MAEs, we applied repetitive transcranial magnetic stimulation (rTMS) to this area during a 10s storage interval and while subjects perceived illusory motion.
Although the visual system rapidly categorizes objects seen under optimal viewing conditions, the categorization of objects seen under impoverished viewing conditions not only requires more time but may also depend more on top-down processing, as hypothesized by object model verification theory. Two studies, one with functional magnetic resonance imaging (fMRI) and one behavioral with the same stimuli, tested this hypothesis. FMRI data were acquired while people categorized more impoverished (MI) and less impoverished (LI) line drawings of objects.
Functional magnetic resonance imaging (fMRI) studies have documented differences between deceptive and honest responses. Capitalizing on this research, companies marketing fMRI-based lie detection services have been founded, generating methodological and ethical concerns in scientific and legal communities. Critically, no fMRI study has examined directly the effect of countermeasures, methods used by prevaricators to defeat deception detection procedures.
Non-invasive techniques for the study of human brain function based on changes of the haemoglobin content or on changes of haemoglobin saturation have recently been proposed. Among the new methods, near-infrared transmission measurements may have significant advantages and complement well-established methods such as functional magnetic resonance imaging and positron emission tomography. Near-infrared measurements can be very fast, comparable in speed to electrophysiological measurements, bur are better localized.
Segmentation of medical images is commonly formulated as a supervised learning problem, where manually labeled training data are summarized using a parametric atlas. Summarizing the data alleviates the computational burden at the expense of possibly losing valuable information on inter-subject variability. This paper presents a novel framework for Supervised Nonparametric Image Parcellation (SNIP). SNIP models the intensity and label images as samples of a joint distribution estimated from the training data in a non-parametric fashion.
Availability of multi-modal magnetic resonance imaging (MRI) databases opens up the opportunity to synthesize different MRI contrasts without actually acquiring the images. In theory such synthetic images have the potential to reduce the amount of acquisitions to perform certain analyses. However, to what extent they can substitute real acquisitions in the respective analyses is an open question. In this study, we used a synthesis method based on patch matching to test whether synthetic images can be useful in segmentation and inter-modality cross-subject registration of brain MRI.
Increasing scan resolution in magnetic resonance imaging is possible with advances in acquisition technology. The increase in resolution, however, comes at the expense of severe image noise. The current approach is to acquire multiple images and average them to restore the lost quality. This approach is expensive as it requires a large number of acquisitions to achieve quality comparable to lower resolution images. We propose an image restoration method for reducing the number of required acquisitions.
Establishing correspondences across brains for the purposes of comparison and group analysis is almost universally done by registering images to one another either directly or via a template. However, there are many registration algorithms to choose from. A recent evaluation of fully automated nonlinear deformation methods applied to brain image registration was restricted to volume-based methods.
In non-rigid registration, the tradeoff between warp regularization and image fidelity is typically determined empirically. In atlas-based segmentation, this leads to a probabilistic atlas of arbitrary sharpness: weak regularization results in well-aligned training images and a sharp atlas; strong regularization yields a "blurry" atlas. In this paper, we employ a generative model for the joint registration and segmentation of images. The atlas construction process arises naturally as estimation of the model parameters.
