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/.

Several brain areas show signal decreases during many different cognitive tasks in functional imaging studies, including the posterior cingulate cortex (PCC) and a medial frontal region incorporating portions of the medial frontal gyrus and ventral anterior cingulate cortex (MFG/vACC). It has been suggested that these areas are components in a default mode network that is engaged during rest and disengaged during cognitive tasks. This study investigated the functional connectivity between the PCC and MFG/vACC during a working memory task and at rest by examining temporal correlations in magnetic resonance signal levels between the regions. The two regions were functionally connected in both conditions. In addition, performance on the working memory task was positively correlated with the strength of this functional connection not only during the working memory task, but also at rest. Thus, it appears these regions are components of a network that may facilitate or monitor cognitive performance, rather than becoming disengaged during cognitive tasks. In addition, these data raise the possibility that the individual differences in coupling strength between these two regions at rest predict differences in cognitive abilities important for this working memory task.

Functional connectivity among brain regions has been investigated via an analysis of correlations between regional signal fluctuations recorded in magnetic resonance (MR) images obtained in a steady state. In comparison with studies of functional connectivity that utilize task manipulations, the analysis of correlations in steady state data is less susceptible to confounds arising when functionally unrelated brain regions respond in similar ways to changes in task. A new approach to identifying interregional correlations in steady state data makes use of two independent data sets. Regions of interest (ROIs) are defined and hypotheses regarding their connectivity are generated in one data set. The connectivity hypotheses are then evaluated in the remaining (independent) data set by analyzing low frequency temporal correlations between regions. The roles of the two data sets are then reversed and the process repeated, perhaps multiple times. This method was illustrated by application to the language system. The existence of a functional connection between Broca's area and Wernicke's area was confirmed in healthy subjects at rest. An increase in this functional connection when the language system was actively engaged (when subjects were continuously listening to narrative text) was also confirmed. In a second iteration of analyses, a correlation between Broca's area and a region in left premotor cortex was found to be significant at rest and to increase during continuous listening. These findings suggest that the proposed methodology can reveal the presence and strength of functional connections in high-level cognitive systems.

Diffusion tensor imaging (DTI) and resting state temporal correlations (RSTC) are two leading techniques for investigating the connectivity of the human brain. They have been widely used to investigate the strength of anatomical and functional connections between distant brain regions in healthy subjects, and in clinical populations. Though they are both based on magnetic resonance imaging (MRI) they have not yet been compared directly. In this work both techniques were employed to create global connectivity matrices covering the whole brain gray matter. This allowed for direct comparisons between functional connectivity measured by RSTC with anatomical connectivity quantified using DTI tractography. We found that connectivity matrices obtained using both techniques showed significant agreement. Connectivity maps created for a priori defined anatomical regions showed significant correlation, and furthermore agreement was especially high in regions showing strong overall connectivity, such as those belonging to the default mode network. Direct comparison between functional RSTC and anatomical DTI connectivity, presented here for the first time, links two powerful approaches for investigating brain connectivity and shows their strong agreement. It provides a crucial multi-modal validation for resting state correlations as representing neuronal connectivity. The combination of both techniques presented here allows for further combining them to provide richer representation of brain connectivity both in the healthy brain and in clinical conditions.

Does the speech motor control system utilize invariant vocal tract shape targets of any kind when producing phonemes? We present a four-part theoretical treatment favoring models whose only invariant targets are auditory perceptual targets over models that posit invariant constriction targets. When combined with earlier theoretical and experimental results (Guenther, 1995a,b; Perkell eta!., 1993; Savariaux et a!., 1995a,b), our hypothesis is that, for vowels and semi-vowels at least, the only invariant targets of the speech production process are multidimensional regions in auditory perceptual space. These auditory perceptual target regions are hypothesized to arise during development as an emergent property of neural map formation in the auditory system (Guenther and Gjaja, 1996), as evidenced by the perceptual magnet effect. Furthermore, speech movements are planned as trajectories in auditory perceptual space. These trajectories are then mapped into articulator movements through a neural mapping that allows motor equivalent variability in constriction locations and degrees when needed, but maintains approximate constriction invariance for a given sound in most instances. These hypotheses are illustrated and substantiated using computer simulations of the DIVA model of speech acquisition and production. Finally, we pose several difficult challenges to proponents of constriction theories based on this theoretical treatment. Speech reference frames