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/.

Although connections between cognitive deficits and age-associated brain differences have been elucidated, relationships with motor performance are less well understood. Here, we broadly review age-related brain differences and motor deficits in older adults in addition to cognition-action theories. Age-related atrophy of the motor cortical regions and corpus callosum may precipitate or coincide with motor declines such as balance and gait deficits, coordination deficits, and movement slowing. Correspondingly, degeneration of neurotransmitter systems-primarily the dopaminergic system-may contribute to age-related gross and fine motor declines, as well as to higher cognitive deficits. In general, older adults exhibit involvement of more widespread brain regions for motor control than young adults, particularly the prefrontal cortex and basal ganglia networks. Unfortunately these same regions are the most vulnerable to age-related effects, resulting in an imbalance of "supply and demand". Existing exercise, pharmaceutical, and motor training interventions may ameliorate motor deficits in older adults.

As the population ages, the need for effective methods to maintain or even improve older adults' cognitive performance becomes increasingly pressing. Here we provide a brief review of the major intervention approaches that have been the focus of past research with healthy older adults (strategy training, multi-modal interventions, cardiovascular exercise, and process-based training), and new approaches that incorporate neuroimaging. As outcome measures, neuroimaging data on intervention-related changes in volume, structural integrity, and functional activation can provide important insights into the nature and duration of an intervention's effects. Perhaps even more intriguingly, several recent studies have used neuroimaging data as a guide to identify core cognitive processes that can be trained in one task with effective transfer to other tasks that share the same underlying processes. Although many open questions remain, this research has greatly increased our understanding of how to promote successful aging of cognition and the brain.

Previous studies of motor learning have described the importance of cognitive processes during the early stages of learning; however, the precise nature of these processes and their neural correlates remains unclear. The present study investigated whether spatial working memory (SWM) contributes to visuomotor adaptation depending on the stage of learning. We tested the hypothesis that SWM would contribute early in the adaptation process by measuring (i) the correlation between SWM tasks and the rate of adaptation, and (ii) the overlap between the neural substrates of a SWM mental rotation task and visuomotor adaptation. Participants completed a battery of neuropsychological tests, a visuomotor adaptation task, and an SWM task involving mental rotation, with the latter two tasks performed in a 3.0-T MRI scanner. Performance on a neuropsychological test of SWM (two-dimensional mental rotation) correlated with the rate of early, but not late, visuomotor adaptation. During the early, but not late, adaptation period, participants showed overlapping brain activation with the SWM mental rotation task, in right dorsolateral prefrontal cortex and the bilateral inferior parietal lobules. These findings suggest that the early, but not late, phase of visuomotor adaptation engages SWM processes.

The cerebellum plays a role in a wide variety of complex behaviors. In order to better understand the role of the cerebellum in human behavior, it is important to know how this structure interacts with cortical and other subcortical regions of the brain. To date, several studies have investigated the cerebellum using resting-state functional connectivity magnetic resonance imaging (fcMRI; Krienen and Buckner, 2009; O'Reilly et al., 2010; Buckner et al., 2011). However, none of this work has taken an anatomically-driven lobular approach. Furthermore, though detailed maps of cerebral cortex and cerebellum networks have been proposed using different network solutions based on the cerebral cortex (Buckner et al., 2011), it remains unknown whether or not an anatomical lobular breakdown best encompasses the networks of the cerebellum. Here, we used fcMRI to create an anatomically-driven connectivity atlas of the cerebellar lobules. Timecourses were extracted from the lobules of the right hemisphere and vermis. We found distinct networks for the individual lobules with a clear division into “motor” and “non-motor” regions. We also used a self-organizing map (SOM) algorithm to parcellate the cerebellum. This allowed us to investigate redundancy and independence of the anatomically identified cerebellar networks. We found that while anatomical boundaries in the anterior cerebellum provide functional subdivisions of a larger motor grouping defined using our SOM algorithm, in the posterior cerebellum, the lobules were made up of sub-regions associated with distinct functional networks. Together, our results indicate that the lobular boundaries of the human cerebellum are not necessarily indicative of functional boundaries, though anatomical divisions can be useful. Additionally, driving the analyses from the cerebellum is key to determining the complete picture of functional connectivity within the structure.

Although sensorimotor adaptation is typically thought of as an implicit form of learning, it has been shown that participants who gain explicit awareness of the nature of the perturbation during adaptation exhibit more learning than those who do not. With rare exceptions, however, explicit awareness is typically polled at the end of the study. Here, we provided participants with either an explicit spatial strategy or no instructions before learning. Early in learning, explicit instructions greatly reduced movement errors but also resulted in increased trial-to-trial variability and longer reaction times. Late in adaptation, performance was indistinguishable between the explicit and implicit groups, but the mechanisms underlying performance improvements remained fundamentally different, as revealed by catch trials. The progression of implicit recalibration in the explicit group was modulated by the use of an explicit strategy: these participants showed a lower level of recalibration as well as decreased aftereffects. This phenomenon may be due to the reduced magnitude of errors made to the target during adaptation or inhibition of implicit learning mechanisms by explicit processing.

Fifteen older adults (M = 68 years old) and 15 young adults (M = 23 years old) participated in a speed-accuracy task in which aiming movements were performed on a digitizing tablet to assess movement slowing and variability in older adults. Target-size and movement amplitude influences were analyzed separately to determine if they affected the performance of the young and older adults differently. When target size was increased, older adults did not increase the relative distance traveled in the primary submovement. When movement amplitude was increased, older adults did not scale movement velocities to the same magnitude as young adults did. Both the inability to scale velocity and the inability to increase the relative distance traveled in the primary submovement contribute to slower, more variable movements observed in older adults depending on task parameters. Thus, these data reveal that manipulation of target size and movement amplitude yield two distinct factors that contribute to slowness of movement in older adults.

It is well documented that both cognitive and motor learning abilities decline with normative aging. Given that cognitive processes such as working memory are engaged during the early stages of motor learning [Anguera, J., Reuter-Lorenz, P., Willingham, D., & Seidler, R. Contributions of spatial working memory to visuomotor learning. Journal of Cognitive Neuroscience, 22(9), 1917-1930, 2010], age-related declines in motor learning may be due in part to reductions in cognitive ability. The present study examined whether age-related declines in spatial working memory (SWM) contribute to deficits in visuomotor adaptation. Young and older adult participants performed a visuomotor adaptation task that involved adapting manual aiming movements to a 30° rotation of the visual feedback display as well as an SWM task in an fMRI scanner. Young adults showed a steeper learning curve than older adults during the early adaptation period. The rate of early adaptation was correlated with SWM performance for the young, but not older, adults. Both groups showed similar brain activation patterns for the SWM task, including engagement of the right dorsolateral prefrontal cortex and bilateral inferior parietal lobules. However, when the SWM activation was used as a limiting mask, younger adults showed neural activation that overlapped with the early adaptation period, whereas older adults did not. A partial correlation controlling for age revealed that the rate of early adaptation correlated with the amount of activation at the right dorsolateral prefrontal cortex. These findings suggest that a failure to effectively engage SWM processes during learning contributes to age-related deficits in visuomotor adaptation.