Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. Highthroughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.

Independent component analysis (ICA) is a promising analysis method that is being increasingly applied to fMRI data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed models of brain activity are not available. Independent component analysis has been successfully utilized to analyze single-subject fMRI data sets, and an extension of this work would be to provide for group inferences. However, unlike univariate methods (e.g., regression analysis, Kolmogorov-Smirnov statistics), ICA does not naturally generalize to a method suitable for drawing inferences about groups of subjects. We introduce a novel approach for drawing group inferences using ICA of fMRI data, and present its application to a simple visual paradigm that alternately stimulates the left or right visual field. Our group ICA analysis revealed task-related components in left and right visual cortex, a transiently task-related component in bilateral occipital/parietal cortex, and a non-task-related component in bilateral visual association cortex. We address issues involved in the use of ICA as an fMRI analysis method such as: (1) How many components should be calculated? (2) How are these components to be combined across subjects? (3) How should the final results be thresholded and/or presented? We show that the methodology we present provides answers to these questions and lay out a process for making group inferences from fMRI data using independent component analysis.

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Diffusion tensor imaging (DTI) is an exciting new MRI modality that can reveal detailed anatomy of the white matter. DTI also allows us to approximate the 3D trajectories of major white matter bundles. By combining the identified tract coordinates with various types of MR parameter maps, such as T 2 and diffusion properties, we can perform tract-specific analysis of these parameters. Unfortunately, 3D tract reconstruction is marred by noise, partial volume effects, and complicated axonal structures. Furthermore, changes in diffusion anisotropy under pathological conditions could alter the results of 3D tract reconstruction. In this study, we created a white matter parcellation atlas based on probabilistic maps of 11 major white matter tracts derived from the DTI data from 28 normal subjects. Using these probabilistic maps, automated tract-specific quantification of fractional anisotropy and mean diffusivity were performed. Excellent correlation was found between the automated and the individual tractography-based results. This tool allows efficient initial screening of the status of multiple white matter tracts.

FMRI studies of response inhibition consistently reveal frontal lobe activation. Localization within the frontal cortex, however, varies across studies and appears dependent on the nature of the task. Activation likelihood estimate (ALE) meta-analysis is a powerful quantitative method of establishing concurrence of activation across functional neuroimaging studies. For this study, ALE was used to investigate concurrent neural correlates of successfully inhibited No-go stimuli across studies of healthy adults performing a Go/No-go task, a paradigm frequently used to measure response inhibition. Due to the potential overlap of neural circuits for response selection and response inhibition, the analysis included only event-related studies contrasting No-go activation with baseline, which allowed for inclusion of all regions that may be critical to visually guided motor response inhibition, including those involved in response selection. These Go/No-go studies were then divided into two groups: "simple" Go/No-go tasks in which the No-go stimulus was always the same, and "complex" Go/No-go tasks, in which the No-go stimulus changed depending on context, requiring frequent updating of stimulus-response associations in working memory. The simple and complex tasks demonstrated distinct patterns of concurrence, with right dorsolateral prefrontal and inferior parietal circuits recruited under conditions of increased working memory demand. Common to both simple and complex Go/No-go tasks was concurrence in the pre-SMA and the left fusiform gyrus. As the pre-SMA has also been shown to be involved in response selection, the results support the notion that the pre-SMA is critical for selection of appropriate behavior, whether selecting to execute an appropriate response or selecting to inhibit an inappropriate response.

Observers viewing a complex visual scene selectively attend to relevant locations or objects and ignore irrelevant ones. Selective attention to an object enhances its neural representation in extrastriate cortex, compared with those of unattended objects, via top-down attentional control signals. The posterior parietal cortex is centrally involved in this control of spatial attention. We examined brain activity during attention shifts using rapid, event-related fMRI of human observers as they covertly shifted attention between two peripheral spatial locations. Activation in extrastriate cortex increased after a shift of attention to the contralateral visual field and remained high during sustained contralateral attention. The time course of activity was substantially different in posterior parietal cortex, where transient increases in activation accompanied shifts of attention in either direction. This result suggests that activation of the parietal cortex is associated with a discrete signal to shift spatial attention, and is not the source of a signal to continuously maintain the current attentive state.

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Independent Component Analysis (ICA) is a technique that attempts to separate data into maximally independent groups. Achieving maximal independence in space or time yields two varieties of ICA meaningful for functional MRI (fMRI) applications: spatial-ICA (SICA) and temporal-ICA (TICA). SICA has so far dominated the application of ICA to fMRI. The objective of these experiments was to study ICA with two predictable components present and evaluate the importance of the underlying independence assumption in the application of ICA. Four novel visual activation paradigms were designed, each consisting of two spatiotemporal components which were either spatially dependent, temporally dependent, both spatially and temporally dependent, or spatially and temporally uncorrelated, respectively. Simulated data were generated and fMRI data from six subjects were acquired using these paradigms. Data from each paradigm were analyzed with regression analysis in order to determine if the signal was occurring as expected. Spatial-and temporal-ICA were then applied to these data, with the general result that ICA found components only where expected, e.g., S(T)ICA "failed" (i.e., yielded independent components unrelated to the "self-evident" components) for paradigms that were spatially (temporally) dependent, and "worked" otherwise. Regression analysis proved a useful "check" for these data, however strong hypotheses will not always be available, and a strength of ICA is that it can characterize data without making specific modeling assumptions. We report a careful examination of some of the assumptions behind ICA methodologies, provide examples of when applying ICA would provide difficult-to-interpret results, and offer suggestions for applying ICA to fMRI data especially when more than one task-related component is present in the data.