The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinates spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP’s acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements for the pipelines.

Using a diverse collection of small molecules generated from a variety of sources, we measured protein-binding activities of each individual compound against each of 100 diverse (sequence-unrelated) proteins using small-molecule microarrays. We also analyzed structural features, including complexity, of the small molecules. We found that compounds from different sources (commercial, academic, natural) have different protein-binding behaviors and that these behaviors correlate with general trends in stereochemical and shape descriptors for these compound collections. Increasing the content of sp 3 -hybridized and stereogenic atoms relative to compounds from commercial sources, which comprise the majority of current screening collections, improved binding selectivity and frequency. The results suggest structural features that synthetic chemists can target when synthesizing screening collections for biological discovery. Because binding proteins selectively can be a key feature of high-value probes and drugs, synthesizing compounds having features identified in this study may result in improved performance of screening collections. S mall-molecule probe-and drug-discovery activities in academia and the pharmaceutical industry often begin with highthroughput screening. Many thousands of small molecules are tested with the expectation that each has potential as a discovery lead. Thus, assembling or synthesizing compound collections for small-molecule screening represents an important step in discovery success, particularly when selecting among compounds from a variety of synthetic and natural sources. Unbiased methods to evaluate the assay performance of compounds from different sources, and to relate performance to chemical structure (defined by computed structural properties) (1, 2), can provide guidance to one element of more valuable small-molecule screening collections.Comparative analyses between compounds often involve cheminformatic analysis of compound structures (3-5) or retrospective analysis of compound performance by mining the literature (6-8) or historical data (9, 10). For example, intermediate molecular complexity has been suggested as theoretically preferable for drug leads (11), and this relationship is supported by evidence mined from historical data (9). In this study, we performed unbiased comparisons of compounds from natural and synthetic sources by first identifying compounds with unknown activities and then exposing them to a common assay platform. We identified a compound collection comprising three subsets: (i) 6,152 compounds from commercial sources that are representative of many common screening collections (commercial compounds; CC); (ii) 6,623 compounds assembled from the academic synthetic chemistry community using, e.g., diversity-oriented synthesis (diverse compounds; DC); and (iii) 2,477 naturally occurring compounds (natural products; NP). We then (i) analyzed distributions of stereochemical and shape complexity for each set;(ii) measured protein-binding activities of each membe...

Computational methods for image-based profiling are under active development, but their success hinges on assays that can capture a wide range of phenotypes. We have developed a multiplex cytological profiling assay that “paints the cell” with as many fluorescent markers as possible without compromising our ability to extract rich, quantitative profiles in high throughput. The assay detects seven major cellular components. In a pilot screen of bioactive compounds, the assay detected a range of cellular phenotypes and it clustered compounds with similar  annotated protein targets or chemical structure based on cytological profiles. The results demonstrate that the assay captures subtle patterns in the combination of morphological labels, thereby detecting the effects of chemical compounds even though their targets are not stained directly. This image-based assay provides an unbiased approach to characterize compound- and disease-associated cell states to support future probe discovery.

The Human Connectome Project (HCP) has developed protocols, standard operating and quality control procedures, and a suite of informatics tools to enable high throughput data collection, data sharing, automated data processing and analysis, and data mining and visualization. Quality control procedures include methods to maintain data collection consistency over time, to measure head motion, and to establish quantitative modality-specific overall quality assessments. Database services developed as customizations of the XNAT imaging informatics platform support both internal daily operations and open access data sharing. The Connectome Workbench visualization environment enables user interaction with HCP data and is increasingly integrated with the HCP's database services. Here we describe the current state of these procedures and tools and their application in the ongoing HCP study.

The spatio-temporal receptive fields (RFs) of cells in the macaque monkey lateral geniculate nucleus (LGN) and striate cortex (V1) have been examined and two distinct sub-populations of non-directional V1 cells have been found: those with a slow largely monophasic temporal RF, and those with a fast very biphasic temporal response. These two sub-populations are in temporal quadrature, the fast biphasic cells crossing over from one response phase to the reverse just as the slow monophasic cells reach their peak response. The two sub-populations also differ in the spatial phases of their RFs. A principal components analysis of the spatio-temporal RFs of directional V1 cells shows that their RFs could be constructed by a linear combination of two components, one of which has the temporal and spatial characteristics of a fast biphasic cell, and the other the temporal and spatial characteristics of a slow monophasic cell. Magnocellular LGN cells are fast and biphasic and lead the fast-biphasic V1 subpopulation by 7 ms; parvocellular LGN cells are slow and largely monophasic and lead the slow monophasic V1 sub-population by 12 ms. We suggest that directional V1 cells get inputs in the approximate temporal and spatial quadrature required for motion detection by combining signals from the two non-directional cortical sub-populations which have been identified, and that these sub-populations have their origins in magno and parvo LGN cells, respectively.