Motivated by the vast amount of information that is rapidly accumulating about the human brain in digital form, we embarked upon a program in 1992 to develop a four-dimensional probabilistic atlas and reference system for the human brain. Through an International Consortium for Brain Mapping (ICBM) a dataset is being collected that includes 7000 subjects between the ages of eighteen and ninety years and including 342 mono-and dizygotic twins. Data on each subject includes detailed demographic, clinical, behavioural and imaging information. DNA has been collected for genotyping from 5800 subjects. A component of the programme uses post-mortem tissue to determine the probabilistic distribution of microscopic cyto-and chemoarchitectural regions in the human brain. This, combined with macroscopic information about structure and function derived from subjects in vivo, provides the ¢rst large scale opportunity to gain meaningful insights into the concordance or discordance in micro-and macroscopic structure and function. The philosophy, strategy, algorithm development, data acquisition techniques and validation methods are described in this report along with database structures. Examples of results are described for the normal adult human brain as well as examples in patients with Alzheimer's disease and multiple sclerosis. The ability to quantify the variance of the human brain as a function of age in a large population of subjects for whom data is also available about their genetic composition and behaviour will allow for the ¢rst assessment of cerebral genotype^phenotype^behavioural correlations in humans to take place in a population this large. This approach and its application should provide new insights and opportunities for investigators interested in basic neuroscience, clinical diagnostics and the evaluation of neuropsychiatric disorders in patients.

Background A persistent debate in psychiatry concerns whether schizophrenia and bipolar disorder are the clinical realizations of discrete versus shared etiological processes. Methods We linked the Multi-Generation Register, containing information about children and their parents of all Swedes, and the Hospital Discharge Register, covering all public psychiatric inpatient hospitalizations in Sweden. We identified 9,009,202 unique individuals in more than 2 million nuclear families. Risks for schizophrenia, bipolar disorder and their co-morbidity were calculated for biological and adoptive parents, offspring, full siblings and half-siblings of probands with the diseases. A multivariate generalized linear mixed model was used to estimate genetic and environmental contributions to liability for schizophrenia, bipolar disorder, and their co-morbidity. Findings There were increased risks of both schizophrenia and bipolar disorder to first degree relatives of probands with either disorder. Half-sibs had a significantly increased risk, albeit substantially lower than the full-siblings. When relatives of probands with bipolar disorder were analysed, increased risks for schizophrenia were present for all relationships, including offspring adopted away. Heritability for schizophrenia was 64% and for bipolar disorder 59%. Shared environmental effects were small but significant for both disorders. The co-morbidity between the disorders was primarily (63%) due to additive genetic effects common to both disorders. Interpretation Similar to molecular genetic studies, we found compelling evidence that schizophrenia and bipolar disorder partially share a common genetic etiology. These results challenge the current nosological dichotomy between schizophrenia and bipolar disorder, and are consistent with a reappraisal of these disorders as distinct diagnostic entities.

Intelligence is highly heritable and a major determinant of human health and well-being. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

Here we report on detailed three-dimensional maps revealing how brain structure is influenced by individual genetic differences. A genetic continuum was detected in which brain structure was increasingly similar in subjects with increasing genetic affinity. Genetic factors significantly influenced cortical structure in Broca's and Wernicke's language areas, as well as frontal brain regions (r2(MZ) > 0.8, p < 0.05). Preliminary correlations were performed suggesting that frontal gray matter differences may be linked to Spearman's g, which measures successful test performance across multiple cognitive domains (p < 0.05). These genetic brain maps reveal how genes determine individual differences, and may shed light on the heritability of cognitive and linguistic skills, as well as genetic liability for diseases that affect the human cortex.