Interactive Exploration of Neuroanatomical Meta-Spaces

Large-archives of neuroimaging data present many opportunities for re-analysis and mining that can lead to new findings of use in basic research or in the characterization of clinical syndromes. However, interaction with such archives tends to be driven textually, based on subject or image volume meta-data, not the actual neuroanatomical morphology itself, for which the imaging was performed to measure. What is needed is a content-driven approach for examining not only the image content itself but to explore brains that are anatomically similar, and identifying patterns embedded within entire sets of neuroimaging data. With the aim of visual navigation of large- scale neurodatabases, we introduce the concept of brain meta-spaces. The meta-space encodes pair-wise dissimilarities between all individuals in a population and shows the relationships between brains as a navigable framework for exploration. We employ multidimensional scaling (MDS) to implement meta-space processing for a new coordinate system that distributes all data points (brain surfaces) in a common frame-of-reference, with anatomically similar brain data located near each other. To navigate within this derived meta-space, we have developed a fully interactive 3D visualization environment that allows users to examine hundreds of brains simultaneously, visualize clusters of brains with similar characteristics, zoom in on particular instances, and examine the surface topology of an individual brain's surface in detail. The visualization environment not only displays the dissimilarities between brains, but also renders complete surface representations of individual brain structures, allowing an instant 3D view of the anatomies, as well as their differences. The data processing is implemented in a grid-based setting using the LONI Pipeline workflow environment. Additionally users can specify a range of baseline brain atlas spaces as the underlying scale for comparative analyses. The novelty in our approach lies in the user ability to simultaneously view and interact with many brains at once but doing so in a vast meta-space that encodes (dis) similarity in morphometry. We believe that the concept of brain meta-spaces has important implications for the future of how users interact with large-scale archives of primary neuroimaging data

Visual Systems for Interactive Exploration and Mining of Large-Scale Neuroimaging Data Archives

While technological advancements in neuroimaging scanner engineering have improved the efficiency of data acquisition, electronic data capture methods will likewise significantly expedite the populating of large-scale neuroimaging databases. As they do and these archives grow in size, a particular challenge lies in examining and interacting with the information that these resources contain through the development of compelling, user-driven approaches for data exploration and mining. In this article, we introduce the informatics visualization for neuroimaging (INVIZIAN) framework for the graphical rendering of, and dynamic interaction with the contents of large-scale neuroimaging data sets. We describe the rationale behind INVIZIAN, detail its development, and demonstrate its usage in examining a collection of over 900 T1-anatomical magnetic resonance imaging (MRI) image volumes from across a diverse set of clinical neuroimaging studies drawn from a leading neuroimaging database. Using a collection of cortical surface metrics and means for examining brain similarity, INVIZIAN graphically displays brain surfaces as points in a coordinate space and enables classification of clusters of neuroanatomically similar MRI images and data mining. As an initial step toward addressing the need for such user-friendly tools, INVIZIAN provides a highly unique means to interact with large quantities of electronic brain imaging archives in ways suitable for hypothesis generation and data mining

Representative factor generation for the interactive visual analysis of high-dimensional data

Datasets with a large number of dimensions per data item (hundreds or more) are challenging both for computational and visual analysis. Moreover, these dimensions have different characteristics and relations that result in sub-groups and/or hierarchies over the set of dimensions. Such structures lead to heterogeneity within the dimensions. Although the consideration of these structures is crucial for the analysis, most of the available analysis methods discard the heterogeneous relations among the dimensions. In this paper, we introduce the construction and utilization of representative factors for the interactive visual analysis of structures in high-dimensional datasets. First, we present a selection of methods to investigate the sub-groups in the dimension set and associate representative factors with those groups of dimensions. Second, we introduce how these factors are included in the interactive visual analysis cycle together with the original dimensions. We then provide the steps of an analytical procedure that iteratively analyzes the datasets through the use of representative factors. We discuss how our methods improve the reliability and interpretability of the analysis process by enabling more informed selections of computational tools. Finally, we demonstrate our techniques on the analysis of brain imaging study results that are performed over a large group of subjects

Application of Neuroanatomical Ontologies for Neuroimaging Data Annotation

The annotation of functional neuroimaging results for data sharing and re-use is particularly challenging, due to the diversity of terminologies of neuroanatomical structures and cortical parcellation schemes. To address this challenge, we extended the Foundational Model of Anatomy Ontology (FMA) to include cytoarchitectural, Brodmann area labels, and a morphological cortical labeling scheme (e.g., the part of Brodmann area 6 in the left precentral gyrus). This representation was also used to augment the neuroanatomical axis of RadLex, the ontology for clinical imaging. The resulting neuroanatomical ontology contains explicit relationships indicating which brain regions are “part of” which other regions, across cytoarchitectural and morphological labeling schemas. We annotated a large functional neuroimaging dataset with terms from the ontology and applied a reasoning engine to analyze this dataset in conjunction with the ontology, and achieved successful inferences from the most specific level (e.g., how many subjects showed activation in a subpart of the middle frontal gyrus) to more general (how many activations were found in areas connected via a known white matter tract?). In summary, we have produced a neuroanatomical ontology that harmonizes several different terminologies of neuroanatomical structures and cortical parcellation schemes. This neuroanatomical ontology is publicly available as a view of FMA at the Bioportal website1. The ontological encoding of anatomic knowledge can be exploited by computer reasoning engines to make inferences about neuroanatomical relationships described in imaging datasets using different terminologies. This approach could ultimately enable knowledge discovery from large, distributed fMRI studies or medical record mining

The enactive approach to architectural experience: A neurophysiological perspective on embodiment, motivation, and affordances

From the enactivist perspective, the way people perceptually experience the world, including architectural spaces, is governed by the dynamic sensorimotor activity of the human organism as a whole and is thereby influenced by the particular conditions of man’s embodiment

Active imaginative listening—a neuromusical critique

The parallel study of music in science and creative practice can be traced back to the ancients; and paralleling the emergence of music neuroscience, creative musical practitioners have employed neurobiological phenomena extensively in music composition and performance. Several examples from the author’s work in this area, which began in the 1960s, are cited and briefly described. From this perspective, the author also explores questions pertinent to current agendas evident in music neuroscience and speculates on potentially potent future directions

Annual Highlights of Results from the International Space Station October 1, 2017 - October 1, 2018

The International Space Station (ISS) is a unique place a convergence of science, technology and human innovation that demonstrates new technologies and makes research breakthroughs that cannot be accomplished on Earth. As an international laboratory for scientific research in microgravity, the space stations international crew lives and works while traveling at a speed of about five miles per second as they make new discoveries in the disciplines of biology and biotechnology, Earth and space science, human research, physical science, educational activities, and technology development and demonstrations