The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository.

This data descriptor describes a repository of openly shared data from an experiment to assess inter-individual differences in default mode network (DMN) activity. This repository includes cross-sectional functional magnetic resonance imaging (fMRI) data from the Multi Source Interference Task, to assess DMN deactivation, the Moral Dilemma Task, to assess DMN activation, a resting state fMRI scan, and a DMN neurofeedback paradigm, to assess DMN modulation, along with accompanying behavioral and cognitive measures. We report technical validation from n=125 participants of the final targeted sample of 180 participants. Each session includes acquisition of one whole-brain anatomical scan and whole-brain echo-planar imaging (EPI) scans, acquired during the aforementioned tasks and resting state. The data includes several self-report measures related to perseverative thinking, emotion regulation, and imaginative processes, along with a behavioral measure of rapid visual information processing. Technical validation of the data confirms that the tasks deactivate and activate the DMN as expected. Group level analysis of the neurofeedback data indicates that the participants are able to modulate their DMN with considerable inter-subject variability. Preliminary analysis of behavioral responses and specifically self-reported sleep indicate that as many as 73 participants may need to be excluded from an analysis depending on the hypothesis being tested. The present data are linked to the enhanced Nathan Kline Institute, Rockland Sample and builds on the comprehensive neuroimaging and deep phenotyping available therein. As limited information is presently available about individual differences in the capacity to directly modulate the default mode network, these data provide a unique opportunity to examine DMN modulation ability in relation to numerous phenotypic characteristics.

A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets.

In this paper we propose a web-based approach for quick visualization of big data from brain magnetic resonance imaging (MRI) scans using a combination of an automated image capture and processing system, nonlinear embedding, and interactive data visualization tools. We draw upon thousands of MRI scans captured via the COllaborative Imaging and Neuroinformatics Suite (COINS). We then interface the output of several analysis pipelines based on structural and functional data to a t-distributed stochastic neighbor embedding (t-SNE) algorithm which reduces the number of dimensions for each scan in the input data set to two dimensions while preserving the local structure of data sets. Finally, we interactively display the output of this approach via a web-page, based on data driven documents (D3) JavaScript library. Two distinct approaches were used to visualize the data. In the first approach, we computed multiple quality control (QC) values from pre-processed data, which were used as inputs to the t-SNE algorithm. This approach helps in assessing the quality of each data set relative to others. In the second case, computed variables of interest (e.g., brain volume or voxel values from segmented gray matter images) were used as inputs to the t-SNE algorithm. This approach helps in identifying interesting patterns in the data sets. We demonstrate these approaches using multiple examples from over 10,000 data sets including (1) quality control measures calculated from phantom data over time, (2) quality control data from human functional MRI data across various studies, scanners, sites, (3) volumetric and density measures from human structural MRI data across various studies, scanners and sites. Results from (1) and (2) show the potential of our approach to combine t-SNE data reduction with interactive color coding of variables of interest to quickly identify visually unique clusters of data (i.e., data sets with poor QC, clustering of data by site) quickly. Results from (3) demonstrate interesting patterns of gray matter and volume, and evaluate how they map onto variables including scanners, age, and gender. In sum, the proposed approach allows researchers to rapidly identify and extract meaningful information from big data sets. Such tools are becoming increasingly important as datasets grow larger.

COINS Data Exchange: An open platform for compiling, curating, and disseminating neuroimaging data.

Neuroimaging data collection is inherently expensive. Maximizing the return on investment in neuroimaging studies requires that neuroimaging data be re-used whenever possible. In an effort to further scientific knowledge, the COINS Data Exchange (DX) (http://coins.mrn.org/dx) aims to make data sharing seamless and commonplace. DX takes a three-pronged approach towards improving the overall state of data sharing within the neuroscience community. The first prong is compiling data into one location that has been collected from all over the world in many different formats. The second prong is curating the data so that it can be stored in one consistent format and so that data QA/QC measures can be assured. The third prong is disseminating the data so that it is easy to consume and straightforward to interpret. This paper explains the concepts behind each prong and describes some challenges and successes that the Data Exchange has experienced.

Harnessing modern web application technology to create intuitive and efficient data visualization and sharing tools.

Neuroscientists increasingly need to work with big data in order to derive meaningful results in their field. Collecting, organizing and analyzing this data can be a major hurdle on the road to scientific discovery. This hurdle can be lowered using the same technologies that are currently revolutionizing the way that cultural and social media sites represent and share information with their users. Web application technologies and standards such as RESTful webservices, HTML5 and high-performance in-browser JavaScript engines are being utilized to vastly improve the way that the world accesses and shares information. The neuroscience community can also benefit tremendously from these technologies. We present here a web application that allows users to explore and request the complex datasets that need to be shared among the neuroimaging community. The COINS (Collaborative Informatics and Neuroimaging Suite) Data Exchange uses web application technologies to facilitate data sharing in three phases: Exploration, Request/Communication, and Download. This paper will focus on the first phase, and how intuitive exploration of large and complex datasets is achieved using a framework that centers around asynchronous client-server communication (AJAX) and also exposes a powerful API that can be utilized by other applications to explore available data. First opened to the neuroscience community in August 2012, the Data Exchange has already provided researchers with over 2500 GB of data.

Automated collection of imaging and phenotypic data to centralized and distributed data repositories.

Accurate data collection at the ground level is vital to the integrity of neuroimaging research. Similarly important is the ability to connect and curate data in order to make it meaningful and sharable with other investigators. Collecting data, especially with several different modalities, can be time consuming and expensive. These issues have driven the development of automated collection of neuroimaging and clinical assessment data within COINS (Collaborative Informatics and Neuroimaging Suite). COINS is an end-to-end data management system. It provides a comprehensive platform for data collection, management, secure storage, and flexible data retrieval (Bockholt et al., 2010; Scott et al., 2011). It was initially developed for the investigators at the Mind Research Network (MRN), but is now available to neuroimaging institutions worldwide. Self Assessment (SA) is an application embedded in the Assessment Manager (ASMT) tool in COINS. It is an innovative tool that allows participants to fill out assessments via the web-based Participant Portal. It eliminates the need for paper collection and data entry by allowing participants to submit their assessments directly to COINS. Instruments (surveys) are created through ASMT and include many unique question types and associated SA features that can be implemented to help the flow of assessment administration. SA provides an instrument queuing system with an easy-to-use drag and drop interface for research staff to set up participants' queues. After a queue has been created for the participant, they can access the Participant Portal via the internet to fill out their assessments. This allows them the flexibility to participate from home, a library, on site, etc. The collected data is stored in a PostgresSQL database at MRN. This data is only accessible by users that have explicit permission to access the data through their COINS user accounts and access to MRN network. This allows for high volume data collection and with minimal user access to PHI (protected health information). An added benefit to using COINS is the ability to collect, store and share imaging data and assessment data with no interaction with outside tools or programs. All study data collected (imaging and assessment) is stored and exported with a participant's unique subject identifier so there is no need to keep extra spreadsheets or databases to link and keep track of the data. Data is easily exported from COINS via the Query Builder and study portal tools, which allow fine grained selection of data to be exported into comma separated value file format for easy import into statistical programs. There is a great need for data collection tools that limit human intervention and error while at the same time providing users with intuitive design. COINS aims to be a leader in database solutions for research studies collecting data from several different modalities.

In vivo differentiation of complementary contrast media at dual-energy CT.

PURPOSE: To evaluate the feasibility of using a commercially available clinical dual-energy computed tomographic (CT) scanner to differentiate the in vivo enhancement due to two simultaneously administered contrast media with complementary x-ray attenuation ratios. MATERIALS AND METHODS: Approval from the institutional animal care and use committee was obtained, and National Institutes of Health guidelines for the care and use of laboratory animals were observed. Dual-energy CT was performed in a set of iodine and tungsten solution phantoms and in a rabbit in which iodinated intravenous and bismuth subsalicylate oral contrast media were administered. In addition, a second rabbit was studied after intravenous administration of iodinated and tungsten cluster contrast media. Images were processed to produce virtual monochromatic images that simulated the appearance of conventional single-energy scans, as well as material decomposition images that separate the attenuation due to each contrast medium. RESULTS: Clear separation of each of the contrast media pairs was seen in the phantom and in both in vivo animal models. Separation of bowel lumen from vascular contrast medium allowed visualization of bowel wall enhancement that was obscured by intraluminal bowel contrast medium on conventional CT scans. Separation of two vascular contrast media in different vascular phases enabled acquisition of a perfectly coregistered CT angiogram and venous phase-enhanced CT scan simultaneously in a single examination. CONCLUSION: Commercially available clinical dual-energy CT scanners can help differentiate the enhancement of selected pairs of complementary contrast media in vivo.

COINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets.

The availability of well-characterized neuroimaging data with large numbers of subjects, especially for clinical populations, is critical to advancing our understanding of the healthy and diseased brain. Such data enables questions to be answered in a much more generalizable manner and also has the potential to yield solutions derived from novel methods that were conceived after the original studies' implementation. Though there is currently growing interest in data sharing, the neuroimaging community has been struggling for years with how to best encourage sharing data across brain imaging studies. With the advent of studies that are much more consistent across sites (e.g., resting functional magnetic resonance imaging, diffusion tensor imaging, and structural imaging) the potential of pooling data across studies continues to gain momentum. At the mind research network, we have developed the collaborative informatics and neuroimaging suite (COINS; http://coins.mrn.org) to provide researchers with an information system based on an open-source model that includes web-based tools to manage studies, subjects, imaging, clinical data, and other assessments. The system currently hosts data from nine institutions, over 300 studies, over 14,000 subjects, and over 19,000 MRI, MEG, and EEG scan sessions in addition to more than 180,000 clinical assessments. In this paper we provide a description of COINS with comparison to a valuable and popular system known as XNAT. Although there are many similarities between COINS and other electronic data management systems, the differences that may concern researchers in the context of multi-site, multi-organizational data sharing environments with intuitive ease of use and PHI security are emphasized as important attributes.

Single electron transfer-promoted photocyclization reactions of linked acceptor-polydonor systems: effects of chain length and type on the efficiencies of macrocyclic ring-forming photoreactions of tethered alpha-silyl ether phthalimide substrates.

Results of an investigation, aimed at gaining information about the factors governing the efficiencies of single electron transfer (SET)-promoted photocyclization reactions of linked acceptor-polydonor systems, are described. One set of substrates used in this effort includes alpha-trimethylsilyl ether terminated, polymethylene- and polyethylenoxy-tethered phthalimides and 2,3-naphthalimides. Photocyclization reactions of the polyethylenoxy-linked phthalimides and naphthalimides were observed to take place in higher chemical yields and with larger quantum efficiencies than those of analogs containing polymethylene tethers of near equal length. These findings show that the rates of formation of 1,omega-zwitterionic biradicals that serve as key intermediates in the photocyclization processes are enhanced in substances that contain oxygen donor sites in the chain. The findings suggest that these donor sites facilitate both initial SET to acceptor excited states and ensuing intrachain SET, resulting in migration of the cation radical center to the terminal alpha-trimethylsilyl ether position. In addition, an inverse relationship was observed between the quantum yields of photocyclization reactions of the tethered phthalimides and naphthalimides and the length of the polyethylenoxy chain. Finally, the roles played by chain type and length in governing photoreaction efficiencies were investigated by using intramolecular competition in photoreactions of polyethylenoxy and polymethylene bis-tethered phthalimides. Mechanistic interpretations and synthetic consequences of the observations made in this study are discussed.

Beta-lactam-forming photochemical reactions of N-trimethylsilylmethyl- and N-tributylstannylmethyl-substituted alpha-ketoamides.

Two mechanisms have been proposed for the beta-lactam-forming photochemical reactions of alpha-ketoamides. One, suggested by Aoyama, involves excited-state H-atom abstraction while the other, put forth by Whitten, follows a sequential SET-proton-transfer route. The photochemical properties of N-trimethylsilylmethyl- and N-tributylstannylmethyl-substituted alpha-ketoamides were explored in order to gain information about the mechanism of this process and to develop a regioselective method for beta-lactam formation. The results of this effort show that (1) photoreactions of N-trimethylsilylmethyl-substituted alpha-ketoamides proceed by competitive H-atom abstraction and sequential SET-desilylation pathways and (2) a sequential SET-destannylation pathway is preferentially followed in photochemical reactions of the tributylstannylmethyl-substituted alpha-ketoamides.