Shared neural substrates of emotionally enhanced perceptual and mnemonic vividness

It is well known that emotionally salient events are remembered more vividly than mundane ones. Our recent research has demonstrated that such memory vividness is due in part to the subjective experience of emotional events as more perceptually vivid, an effect we call emotion-enhanced vividness, or EEV. The present study built on previously reported research in which fMRI data were collected while participants rated relative levels of visual noise overlaid on emotionally salient and neutral images. Ratings of greater EEV were associated with greater activation in the amygdala, visual cortex, and posterior insula. In the present study, we measured BOLD activation that predicted recognition memory vividness for these same images one week later. Results showed that, after controlling for differences between scenes in low-level objective features, hippocampus activation uniquely predicted subsequent memory vividness. In contrast, amygdala and visual cortex regions that were sensitive to EEV were also modulated by subsequent ratings of memory vividness. These findings suggest shared neural substrates for the influence of emotional salience on perceptual and mnemonic vividness, with amygdala and visual cortex activation at encoding contributing to the experience of both perception and subsequent memory. © 2013 Todd, Schmitz, Susskind and Anderson

Influences of Neural Pathway Integrity on Children's Response to Reading Instruction

As the education field moves toward using responsiveness to intervention to identify students with disabilities, an important question is the degree to which this classification can be connected to a student's neurobiological characteristics. A few functional neuroimaging studies have reported a relationship between activation and response to instruction; however, whether a similar correlation exists with white matter (WM) is not clear. To investigate this issue, we acquired high angular resolution diffusion images from a group of first grade children who differed in their levels of responsiveness to a year-long reading intervention. Using probabilistic tractography, we calculated the strength of WM connections among nine cortical regions of interest and correlated these estimates with participants’ scores on four standardized reading measures. We found eight significant correlations, four of which were connections between the insular cortex and angular gyrus. In each of the correlations, a relationship with children's response to intervention was evident

Psychophysical and neural evidence for emotion-enhanced perceptual vividness

Highly emotional events are associated with vivid flashbulb memories. Here we examine whether the flashbulb metaphor characterizes a previously unknown emotion-enhanced vividness (EEV) during initial perceptual experience. Using a magnitude estimation procedure, human observers estimated the relative magnitude of visual noise overlaid on scenes. After controlling for computational metrics of objective visual salience, emotional salience was associated with decreased noise, or heightened perceptual vividness, demonstrating EEV, which predicted later memory vividness. Event-related potentials revealed a posterior P2 component at ~200 ms that was associated with both increased emotional salience and decreased objective noise levels, consistent with EEV. Blood oxygenation level-dependent response in the lateral occipital complex (LOC), insula, and amygdala predicted online EEV. The LOC and insula represented complimentary influences on EEV, with the amygdala statistically mediating both. These findings indicate that the metaphorical vivid light surrounding emotional memories is embodied directly in perceptual cortices during initial experience, supported by cortico-limbic interactions. © 2012 the authors

Commentary Quantitative MR For Epilepsy: A Clinical and Research Tool?

An explosion in the use of quantitative magnetic resonance (MR) for the investigation of epilepsy has taken place during the last 5 years. Assessment of structural brain changes with MRbased volumetrics ( 1-7) or T2 relaxometry (8-1 0) is an ideal model for integrating research with clinical decision making. These techniques have furthered our understanding not only of brain dysfunction (in this case hippocampal sclerosis) but also of brain function (in this case the hippocampus). Quantitative techniques have found a place in the preoperative assessment of hippocampal sclerosis because of their increased sensitivity over visual inspection (11). Most quantitative MR investigations for epilepsy have concentrated on measuring brain volumes rather than relaxation times. Using this method, the degree of hippocampal atrophy can be quantified and compared with other variables for both research and clinical purposes. Hippocampal atrophy already has been shown to correlate with hippocampal sclerosis, lateralization of the electroencephalographic abnormalities, degree of hippocampal neuronal loss, verbal memory performance, and postoperative seizure control The manuscript by Grunewald et al (8) in this issue of AJNR, as well as a related paper by Jackson et al (9), describes a different quantitative technique for studying epilepsy. They found T2 relaxometry to be a reliable and sensitive method for detecting hippocampal sclerosis (for T2 values greater than 116 milliseconds). An older study by Matsuda et al also used T2 values to uncover hippocampal sclerosis (10). Although these findings are intriguing, one might ask why we should be interested in this quantitative technique for determining hippocampal sclerosis, when a reliable, sensitive, and specific method already exists? The answer lies with the biological factors that are the basis for the MR findings . The underlying mechanism for T2 prolongation may be independent of the mechanism producing the atrophic changes. This could have important implications for the following clinical and research questions: 1. Can T2 values help identify the additional 5% to 15% of patients with hippocampal Address reprint requests to Richard A . Bronen, MD

On the scaling approach to electron-electron interactions in a chaotic quantum dot

A scaling theory is used to study the low energy physics of electron-electron interactions in a double quantum dot. We show that the fact that electrons are delocalized over two quantum dots does not affect the instability criterion for the description of electron-electron interactions in terms of a ``universal interaction Hamiltonian''.Comment: 4 pages, 3 figure

Public policy and the promise of digital credit for financial inclusion

Digital credit products are characterized by a lending process that is instantaneous, automated, and remote. While digital credit has the potential to reach less collateralized, less mobile, and more remote cohorts of borrowers, there are also risks in relying on digital credit for financial inclusion. This paper investigates the digital credit policy environment and the extent to which it may support pro-poor digital credit market development using two types of documents: a set of 23 regulatory documents specifically mentioning either digital or online credit or lending, and another set of 298 informal documents relevant to digital credit based on a systematic web search. After reviewing the literature on the effects of credit expansion and automated credit scoring, we summarize the characteristics of the current digital credit regulatory environment in low- and middle-income countries. Our findings suggest that few regulations specifically target digital credit markets, and that the current regulatory environment may not support the full potential of digital credit to reach historically underserved credit consumers. Most countries do not explicitly target financial inclusion as part of their digital credit policies. However, we do find evidence that informal web documents consider financial inclusion to a greater extent than formal regulatory documents

pvlib iotools—Open-source Python functions for seamless access to solar irradiance data

Access to accurate solar resource data is critical for numerous applications, including estimating the yield of solar energy systems, developing radiation models, and validating irradiance datasets. However, lack of standardization in data formats and access interfaces across providers constitutes a major barrier to entry for new users. pvlib python's iotools subpackage aims to solve this issue by providing standardized Python functions for reading local files and retrieving data from external providers. All functions follow a uniform pattern and return convenient data outputs, allowing users to seamlessly switch between data providers and explore alternative datasets. The pvlib package is community-developed on GitHub: https://github.com/pvlib/pvlib-python. As of pvlib python version 0.9.5, the iotools subpackage supports 12 different datasets, including ground measurement, reanalysis, and satellite-derived irradiance data. The supported ground measurement networks include the Baseline Surface Radiation Network (BSRN), NREL MIDC, SRML, SOLRAD, SURFRAD, and the US Climate Reference Network (CRN). Additionally, satellite-derived and reanalysis irradiance data from the following sources are supported: PVGIS (SARAH &amp; ERA5), NSRDB PSM3, and CAMS Radiation Service (including McClear clear-sky irradiance).</p

pvlib iotools—Open-source Python functions for seamless access to solar irradiance data

Access to accurate solar resource data is critical for numerous applications, including estimating the yield of solar energy systems, developing radiation models, and validating irradiance datasets. However, lack of standardization in data formats and access interfaces across providers constitutes a major barrier to entry for new users. pvlib python's iotools subpackage aims to solve this issue by providing standardized Python functions for reading local files and retrieving data from external providers. All functions follow a uniform pattern and return convenient data outputs, allowing users to seamlessly switch between data providers and explore alternative datasets. The pvlib package is community-developed on GitHub: https://github.com/pvlib/pvlib-python. As of pvlib python version 0.9.5, the iotools subpackage supports 12 different datasets, including ground measurement, reanalysis, and satellite-derived irradiance data. The supported ground measurement networks include the Baseline Surface Radiation Network (BSRN), NREL MIDC, SRML, SOLRAD, SURFRAD, and the US Climate Reference Network (CRN). Additionally, satellite-derived and reanalysis irradiance data from the following sources are supported: PVGIS (SARAH &amp; ERA5), NSRDB PSM3, and CAMS Radiation Service (including McClear clear-sky irradiance).</p