Aging is associated with declines in motor and somatosensory function. Some of these motor declines have been linked to age-related reductions in inhibitory function. Here we examined whether... Show moreAging is associated with declines in motor and somatosensory function. Some of these motor declines have been linked to age-related reductions in inhibitory function. Here we examined whether tactile surround inhibition also changes with age and whether these changes are associated with those in the motor domain. We tested a group of 56 participants spanning a wide age range (18–76 years old), allowing us to examine when age differences emerge across the lifespan. Participants performed tactile and motor tasks that have previously been linked to inter- and intra-hemispheric inhibition in the somatosensory and motor systems. The results showed that aging is associated with reductions in inhibitory function in both the tactile and motor systems starting around 40 years of age; however, age effects in the two systems were not correlated. The independent effects of age on tactile and motor inhibitory function suggest that distinct mechanisms may underlie age-related reductions in inhibition in the somatosensory and motor systems. Show less
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... Show moreAlthough 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. High-throughput 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/. Show less
