The short and long of it: neural correlates of temporal-order memory for autobiographical events

Previous functional neuroimaging studies of temporal-order memory have investigated memory for laboratory stimuli that are causally unrelated and poor in sensory detail. In contrast, the present functional magnetic resonance imaging (fMRI) study investigated temporal-order memory for autobiographical events that were causally interconnected and rich in sensory detail. Participants took photographs at many campus locations over a period of several hours, and the following day they were scanned while making temporal-order judgments to pairs of photographs from different locations. By manipulating the temporal lag between the two locations in each trial, we compared the neural correlates associated with reconstruction processes, which we hypothesized depended on recollection and contribute mainly to short lags, and distance processes, which we hypothesized to depend on familiarity and contribute mainly to longer lags. Consistent with our hypotheses, parametric fMRI analyses linked shorter lags to activations in regions previously associated with recollection (left prefrontal, parahippocampal, precuneus, and visual cortices), and longer lags with regions previously associated with familiarity (right prefrontal cortex). The hemispheric asymmetry in prefrontal cortex activity fits very well with evidence and theories regarding the contributions of the left versus right prefrontal cortex to memory (recollection vs. familiarity processes) and cognition (systematic vs. heuristic processes). In sum, using a novel photo-paradigm, this study provided the first evidence regarding the neural correlates of temporal-order for autobiographical events

Component Neural Systems for the Creation of Emotional Memories during Free Viewing of a Complex, Real-World Event

To investigate the neural systems that contribute to the formation of complex, self-relevant emotional memories, dedicated fans of rival college basketball teams watched a competitive game while undergoing functional magnetic resonance imaging (fMRI). During a subsequent recognition memory task, participants were shown video clips depicting plays of the game, stemming either from previously-viewed game segments (targets) or from non-viewed portions of the same game (foils). After an old–new judgment, participants provided emotional valence and intensity ratings of the clips. A data driven approach was first used to decompose the fMRI signal acquired during free viewing of the game into spatially independent components. Correlations were then calculated between the identified components and post-scanning emotion ratings for successfully encoded targets. Two components were correlated with intensity ratings, including temporal lobe regions implicated in memory and emotional functions, such as the hippocampus and amygdala, as well as a midline fronto-cingulo-parietal network implicated in social cognition and self-relevant processing. These data were supported by a general linear model analysis, which revealed additional valence effects in fronto-striatal-insular regions when plays were divided into positive and negative events according to the fan's perspective. Overall, these findings contribute to our understanding of how emotional factors impact distributed neural systems to successfully encode dynamic, personally-relevant event sequences

The caBIG™ Annotation and Image Markup Project

Image annotation and markup are at the core of medical interpretation in both the clinical and the research setting. Digital medical images are managed with the DICOM standard format. While DICOM contains a large amount of meta-data about whom, where, and how the image was acquired, DICOM says little about the content or meaning of the pixel data. An image annotation is the explanatory or descriptive information about the pixel data of an image that is generated by a human or machine observer. An image markup is the graphical symbols placed over the image to depict an annotation. While DICOM is the standard for medical image acquisition, manipulation, transmission, storage, and display, there are no standards for image annotation and markup. Many systems expect annotation to be reported verbally, while markups are stored in graphical overlays or proprietary formats. This makes it difficult to extract and compute with both of them. The goal of the Annotation and Image Markup (AIM) project is to develop a mechanism, for modeling, capturing, and serializing image annotation and markup data that can be adopted as a standard by the medical imaging community. The AIM project produces both human- and machine-readable artifacts. This paper describes the AIM information model, schemas, software libraries, and tools so as to prepare researchers and developers for their use of AIM

Underwater microscope for measuring spatial and temporal changes in bed-sediment grain size

This paper is not subject to U.S. copyright. The definitive version was published in Sedimentary Geology 202 (2007): 402-408, doi:10.1016/j.sedgeo.2007.03.020.For more than a century, studies of sedimentology and sediment transport have measured bed-sediment grain size by collecting samples and transporting them back to the laboratory for grain-size analysis. This process is slow and expensive. Moreover, most sampling systems are not selective enough to sample only the surficial grains that interact with the flow; samples typically include sediment from at least a few centimeters beneath the bed surface. New hardware and software are available for in situ measurement of grain size. The new technology permits rapid measurement of surficial bed sediment. Here we describe several systems we have deployed by boat, by hand, and by tripod in rivers, oceans, and on beaches

The Location and Appearance of Second Malignancies in Patients With Bilateral Retinoblastoma

Purpose. This paper describes the clinical history and radiographic appearance of second malignancies in patients with bilateral retinoblastoma

The Asymptotic Number of Attractors in the Random Map Model

The random map model is a deterministic dynamical system in a finite phase space with n points. The map that establishes the dynamics of the system is constructed by randomly choosing, for every point, another one as being its image. We derive here explicit formulas for the statistical distribution of the number of attractors in the system. As in related results, the number of operations involved by our formulas increases exponentially with n; therefore, they are not directly applicable to study the behavior of systems where n is large. However, our formulas lend themselves to derive useful asymptotic expressions, as we show.Comment: 16 pages, 1 figure. Minor changes. To be published in Journal of Physics A: Mathematical and Genera