While the role of cortical microstructure in organising neural function is well established, it remains unclear how structural constraints can give rise to more flexible elements of cognition. While nonhuman primate research has demonstrated a close structure-function correspondence, the relationship between microstructure and function remains poorly understood in humans, in part because of the reliance on post mortem analyses which cannot be directly related to functional data. To overcome this barrier, we developed a novel approach to model the similarity of microstructural profiles sampled in the direction of cortical columns. Our approach was initially formulated based on an ultra-highresolution 3D histological reconstruction of an entire human brain and then translated to myelinsensitive MRI data in a large cohort of healthy adults. This novel method identified a system-level gradient of microstructural differentiation traversing from primary sensory to limbic regions that followed shifts in laminar differentiation and cytoarchitectural complexity. Importantly, while microstructural and functional gradients described a similar hierarchy, they became increasingly dissociated in transmodal default mode and fronto-parietal networks. Meta analytic decoding of these topographic dissociations highlighted involvement in higher-level aspects of cognition such as cognitive control and social cognition. Our findings demonstrate a relative decoupling of macroscale functional from microstructural gradients in transmodal regions, which likely contributes to the flexible role these regions play in human cognition.

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data (http://www.brainchart.io/). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

Neurodevelopmental disorders have a heritable component and are associated with region specific alterations in brain anatomy. However, it is unclear how genetic risks for neurodevelopmental disorders are translated into spatially patterned brain vulnerabilities. Here, we integrated cortical neuroimaging data from patients with neurodevelopmental disorders caused by genomic copy number variations (CNVs) and gene expression data from healthy subjects. For each of the six investigated disorders, we show that spatial patterns of cortical anatomy changes in youth are correlated with cortical spatial expression of CNV genes in neurotypical adults. By transforming normative bulk-tissue cortical expression data into cell-type expression maps, we link anatomical change maps in each analysed disorder to specific cell classes as well as the CNV-region genes they express. Our findings reveal organizing principles that regulate the mapping of genetic risks onto regional brain changes in neurogenetic disorders. Our findings will enable screening for candidate molecular mechanisms from readily available neuroimaging data.

Autism spectrum conditions (autism) affect ~1% of the population and are characterized by deficits in social communication. Oxytocin has been widely reported to affect social-communicative function and its neural underpinnings. Here we report the first evidence that intranasal oxytocin administration improves a core problem that individuals with autism have in using eye contact appropriately in real-world social settings. A randomized double-blind, placebo-controlled, within-subjects design is used to examine how intranasal administration of 24 IU of oxytocin affects gaze behavior for 32 adult males with autism and 34 controls in a real-time interaction with a researcher. This interactive paradigm bypasses many of the limitations encountered with conventional static or computer-based stimuli. Eye movements are recorded using eye tracking, providing an objective measurement of looking patterns. The measure is shown to be sensitive to the reduced eye contact commonly reported in autism, with the autism group spending less time looking to the eye region of the face than controls. Oxytocin administration selectively enhanced gaze to the eyes in both the autism and control groups (transformed mean eye-fixation difference per second=0.082; 95% CI:0.025–0.14, P=0.006). Within the autism group, oxytocin has the most effect on fixation duration in individuals with impaired levels of eye contact at baseline (Cohen's d=0.86). These findings demonstrate that the potential benefits of oxytocin in autism extend to a real-time interaction, providing evidence of a therapeutic effect in a key aspect of social communication.

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Neurodevelopmental disorders are highly heritable and associated with spatially-selective disruptions of brain anatomy. The logic that translates genetic risks into spatially patterned brain vulnerabilities remains unclear but is a fundamental question in disease pathogenesis. Here, we approach this question by integrating (i) in vivo neuroimaging data from patient subgroups with known causal genomic copy number variations (CNVs), and (ii) bulk and single-cell gene expression data from healthy cortex. First, for each of six different CNV disorders, we show that spatial patterns of cortical anatomy change in youth are correlated with spatial patterns of expression for CNV region genes in bulk cortical tissue from typically-developing adults. Next, by transforming normative bulk-tissue cortical expression data into cell-type expression maps, we further link each disorder's anatomical change map to specific cell classes and specific CNV-region genes that these cells express. Finally, we establish convergent validity of this "transcriptional vulnerability model" by inter-relating patient neuroimaging data with measures of altered gene expression in both brain and bloodderived patient tissue. Our work clarifies general biological principles that govern the mapping of genetic risks onto regional brain disruption in neurodevelopmental disorders. We present new methods that can harness these principles to screen for potential cellular and molecular determinants of disease from readily available patient neuroimaging data.

1Anhedonia is a core feature of several psychiatric disorders but its biological underpinnings are 2 poorly understood. We performed a genome-wide association study of anhedonia in 375,275 UK 3 Biobank participants and assessed for genetic correlation between anhedonia and neuropsychiatric 4 conditions (major depressive disorder, schizophrenia, bipolar disorder, obsessive compulsive 5 disorder and Parkinson's Disease). We then used a polygenic risk score approach to test for 6 association between genetic loading for anhedonia and both brain structure and brain function. This 7 included: magnetic resonance imaging (MRI) assessments of total grey matter volume, white matter 8 volume, cerebrospinal fluid volume, and 15 cortical/subcortical regions of interest; diffusion tensor 9 imaging (DTI) measures of white matter tract integrity; and functional MRI activity during an 10 emotion processing task. We identified 11 novel loci associated at genome-wide significance with 11 anhedonia, with a SNP heritability estimate (h 2 SNP) of 5.6%. Strong positive genetic correlations 12 were found between anhedonia and major depressive disorder, schizophrenia and bipolar disorder; 13 but not with obsessive compulsive disorder or Parkinson's Disease. Polygenic risk for anhedonia was 14 associated with poorer brain white matter integrity, smaller total grey matter volume, and smaller 15 volumes of brain regions linked to reward and pleasure processing, including nucleus accumbens, 16 caudate and medial frontal cortex. In summary, the identification of novel anhedonia-associated loci 17 substantially expands our current understanding of the biological basis of anhedonia and genetic 18 correlations with several psychiatric disorders confirm the utility of this trait as a transdiagnostic 19 marker of vulnerability to mental illness. We also provide the first evidence that genetic risk for 20 anhedonia influences brain structure, particularly in regions associated with reward and pleasure 21 processing. 22

Why do people act altruistically? One theory is that empathy is a driver of morality. Experimental studies of this are often confined to laboratory settings, which often lack ecological validity. In the present study we investigated whether empathy traits predict if people will act altruistically in a real-world setting, "in the wild". We staged a situation in public that was designed to elicit helping, and subsequently measured empathic traits in those who either stopped to help or walked past and did not help. Results show that a higher number of empathic traits are a significant and positive predictor for altruistic behavior in a real-life situation. This supports the theory that the act of doing good is correlated with empathy.