The highly complex structure of the human brain is strongly shaped by genetic influences1. Subcortical brain regions form circuits with cortical areas to coordinate movement2, learning, memory3 and motivation4, and altered circuits can lead to abnormal behaviour and disease2. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume5 and intracranial volume6. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10−33; 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability inhuman brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

The ability to detect brain anatomy and pathophysiology with MRI is limited by the contrast-to-noise ratio (CNR), which depends on the contrast mechanism used and the spatial resolution. In this work, we show that in MRI of the human brain, large improvements in contrast to noise in high-resolution images are possible by exploiting the MRI signal phase at high magnetic field strength. Using gradient-echo MRI at 7.0 tesla and a multichannel detector, a nominal voxel size of 0.24 ؋ 0.24 ؋ 1.0 mm 3 (58 nl) was achieved. At this resolution, a strong phase contrast was observed both between as well as within gray matter (GM) and white matter (WM). In gradient-echo phase images obtained on normal volunteers at this high resolution, the CNR between GM and WM ranged from 3:1 to 20:1 over the cortex. This CNR is an almost 10-fold improvement over conventional MRI techniques that do not use image phase, and it is an Ϸ100-fold improvement when including the gains in resolution from high-field and multichannel detection. Within WM, phase contrast appeared to be associated with the major fiber bundles, whereas contrast within GM was suggestive of the underlying layer structure. The observed phase contrast is attributed to local variations in magnetic susceptibility, which, at least in part, appeared to originate from iron stores. The ability to detect cortical substructure from MRI phase contrast at high field is expected to greatly enhance the study of human brain anatomy in vivo.anatomy ͉ contrast mechanism ͉ cortex

The regional distribution of white matter (WM) abnormalities in schizophrenia remains poorly understood, and reported disease effects on the brain vary widely between studies. In an effort to identify commonalities across studies, we perform what we believe is the first ever large-scale coordinated study of WM microstructural differences in schizophrenia. Our analysis consisted of 2359 healthy controls and 1963 schizophrenia patients from 29 independent international studies; we harmonized the processing and statistical analyses of diffusion tensor imaging (DTI) data across sites and meta-analyzed effects across studies. Significant reductions in fractional anisotropy (FA) in schizophrenia patients were widespread, and detected in 20 of 25 regions of interest within a WM skeleton representing all major WM fasciculi. Effect sizes varied by region, peaking at (d=0.42) for the entire WM skeleton, driven more by peripheral areas as opposed to the core WM where regions of interest were defined. The anterior corona radiata (d=0.40) and corpus callosum (d=0.39), specifically its body (d=0.39) and genu (d=0.37), showed greatest effects. Significant decreases, to lesser degrees, were observed in almost all regions analyzed. Larger effect sizes were observed for FA than diffusivity measures; significantly higher mean and radial diffusivity was observed for schizophrenia patients compared with controls. No significant effects of age at onset of schizophrenia or medication dosage were detected. As the largest coordinated analysis of WM differences in a psychiatric disorder to date, the present study provides a robust profile of widespread WM abnormalities in schizophrenia patients worldwide. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.

Heart rate fluctuations occur in the low frequency region (< 0.1 Hz) probed in functional magnetic resonance imaging (fMRI) studies of resting-state functional connectivity and most fMRI block paradigms, and may be related to low frequency blood-oxygenation-level-dependent (BOLD) signal fluctuations. To investigate this hypothesis, temporal correlations between cardiac rate and restingstate fMRI signal timecourses were assessed at 3 Tesla. Resting-state BOLD fMRI and accompanying physiological data were acquired and analyzed using cross-correlation and regression. Time-shifted cardiac rate timecourses were included as regressors in addition to established physiological regressors (RETROICOR (Glover et al., 2000) and respiration volume per unit time (Birn et al., 2006b)). Significant correlations between the cardiac rate and BOLD signal timecourses were revealed, particularly negative correlations in gray matter at time-shifts of 6-12 seconds and positive correlations at time shifts of 30-42 seconds (TR = 6 s). Regressors consisting of cardiac rate timecourses shifted by delays of between 0 and 24 seconds explained an additional 1 % of the BOLD signal variance on average over the whole brain across 9 subjects, a similar additional variance to that explained by respiration volume per unit time and RETROICOR regressors, even when used in combination with these other physiological regressors. This suggests that including such time-shifted cardiac rate regressors will be beneficial for explaining physiological noise variance and will thereby improve the statistical power in future task-based and resting-state fMRI studies.