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This special issue summarizes an impressive body of work concerned with in vivo imaging of brain microstructure. Collectively, papers included here demonstrate the power of multi-modal magnetic resonance imaging (MRI) for mapping various structural properties of brain tissue. In this introduction, I provide a user's perspective vis-à-vis motivations for these efforts, review briefly the cellular composition of grey and white matter in the human brain, and provide a few examples of how we can bridge the gap between ex vivo and in vivo datasets to facilitate interpretation of studies measuring brain macro- and microstructure.

Many psychiatric disorders emerge in adolescence, when profound changes take place in the brain. Paus and colleagues provide an overview of the neurobiological changes that occur during adolescence and discuss their possible relationship to the emergence of psychopathology. The peak age of onset for many psychiatric disorders is adolescence, a time of remarkable physical and behavioural changes. The processes in the brain that underlie these behavioural changes have been the subject of recent investigations. What do we know about the maturation of the human brain during adolescence? Do structural changes in the cerebral cortex reflect synaptic pruning? Are increases in white-matter volume driven by myelination? Is the adolescent brain more or less sensitive to reward? Finding answers to these questions might enable us to further our understanding of mental health during adolescence.

Studies have shown that natural environments can enhance health and here we build upon that work by examining the associations between comprehensive greenspace metrics and health. We focused on a large urban population center (Toronto, Canada) and related the two domains by combining high-resolution satellite imagery and individual tree data from Toronto with questionnaire-based self-reports of general health perception, cardio-metabolic conditions and mental illnesses from the Ontario Health Study. Results from multiple regressions and multivariate canonical correlation analyses suggest that people who live in neighborhoods with a higher density of trees on their streets report significantly higher health perception and significantly less cardio-metabolic conditions (controlling for socio-economic and demographic factors). We find that having 10 more trees in a city block, on average, improves health perception in ways comparable to an increase in annual personal income of $10,000 and moving to a neighborhood with $10,000 higher median income or being 7 years younger. We also find that having 11 more trees in a city block, on average, decreases cardio-metabolic conditions in ways comparable to an increase in annual personal income of $20,000 and moving to a neighborhood with $20,000 higher median income or being 1.4 years younger.

The study of neuroanatomy using MRI enables key insights into how our brains function, are shaped by genes and environment, and how they change with development, aging and disease. The authors provide an overview of the methods for measuring the brain and also describe key artifacts and confounds The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging and disease. Developments in MRI acquisition, image processing and data modeling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro- and mesoscopic structure and for inferring microstructural properties; we also describe key artifacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, although methods need to improve and caution is required in interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works.

FreeSurfer allows one to segment magnetic resonance images (MRIs) into 68 cortical regions and to estimate their cortical thickness, surface area and volume (http://surfer.nmr.mgh.harvard.edu/). The third stage involved averaging values of gene expression across all voxels mapped into a specific FreeSurfer region; for each individual brain, median values were obtained for each gene and each cortical region. Gene Ontology Enrichment Analysis The ErmineJ software (version 3.0.2) was used for Gene Ontology enrichment analyses (Gillis et al., 2010), genes were ranked according to the consistency measure to generate receiver-operator curves for all gene ontology groups in the molecular process and cellular component ontologies with more than 10 and less than 1000 genes (5148 groups)(Ashburner et al., 2000). Given the limited availability of data for the right cerebral hemisphere (2 of 6 individuals), we focus henceforth on the left hemisphere in which gene expression data are available for all six donors (1269 samples mapped to 34 left-hemisphere cortical regions).

Key Points The anterior cingulate gyrus (ACC) has extensive connections with the motor cortex and spinal cord, connections that support the involvement of the ACC in motor control. Microstimulation of the ACC does indeed lead to body movements and to the utterance of vocalizations, whereas lesions of the ACC might lead to deficits in spontaneous initiation of movement and speech. Reciprocal connections between the ACC and the lateral prefrontal cortex (PFC) support a role for the ACC in cognition. Indeed, different studies have supported this idea by revealing the existence of functional and effective connectivity between these two regions during the performance of cognitive tasks. Different ideas have been put forward to explain the involvement of the PFC–ACC interaction in cognition. It has been argued that the PFC computes and maintains information necessary for the choice of an appropriate response, whereas the ACC facilitates implementation of the selected action. Alternatively, it has been proposed that the ACC might be involved in error detection or in conflict monitoring during the execution of a given task. Extensive afferents to the ACC from the midline thalamic nuclei and from the brainstem indicate that arousal states can influence ACC activation. Furthermore, imaging studies have shown that ACC activation covaries with activation of midline thalamic nuclei. In addition, dopamine receptor agonists and antagonists affect ACC activation, highlighting the effect of neuromodulatory transmitter systems on ACC function. The functional overlap of the motor, cognitive and arousal/drive domain might help to distinguish the function of the ACC from that of other cortical regions. In fact, this overlap places the ACC in a unique position to translate intentions to actions, participating in the willed control of behaviour. Controversy surrounds the function of the anterior cingulate cortex. Recent discussions about its role in behavioural control have centred on three main issues: its involvement in motor control, its proposed role in cognition and its relationship with the arousal/drive state of the organism. I argue that the overlap of these three domains is key to distinguishing the anterior cingulate cortex from other frontal regions, placing it in a unique position to translate intentions to actions.

The genetic architecture of testosterone is highly distinct between sexes. Moreover, obesity is associated with higher testosterone in females but lower testosterone in males. Here, we ask whether male-specific testosterone variants are associated with a male pattern of obesity and type 2 diabetes (T2D) in females, and vice versa. In the UK Biobank, we conducted sex-specific genome-wide association studies and computed polygenic scores for total (PGSTT) and bioavailable testosterone (PGSBT). We tested sex-congruent and sex-incongruent associations between sex-specific PGSTs and metabolic traits, as well as T2D diagnosis. Female-specific PGSBT was associated with an elevated cardiometabolic risk and probability of T2D, in both sexes. Male-specific PGSTT was associated with traits conferring a lower cardiometabolic risk and probability of T2D, in both sexes. We demonstrate the value in considering polygenic testosterone as sex-related continuous traits, in each sex.

We recently devised continuous “sex-scores” that sum up multiple quantitative traits, weighted by their respective sex-difference effect sizes, as an approach to estimating polyphenotypic “maleness/femaleness” within each binary sex. To identify the genetic architecture underlying these sex-scores, we conducted sex-specific genome-wide association studies (GWASs) in the UK Biobank cohort (females: n = 161,906; males: n = 141,980). As a control, we also conducted GWASs of sex-specific “sum-scores”, simply aggregating the same traits, without weighting by sex differences. Among GWAS-identified genes, while sum-score genes were enriched for genes differentially expressed in the liver in both sexes, sex-score genes were enriched for genes differentially expressed in the cervix and across brain tissues, particularly for females. We then considered single nucleotide polymorphisms with significantly different effects (sdSNPs) between the sexes for sex-scores and sum-scores, mapping to male-dominant and female-dominant genes. Here, we identified brain-related enrichment for sex-scores, especially for male-dominant genes; these findings were present but weaker for sum-scores. Genetic correlation analyses of sex-biased diseases indicated that both sex-scores and sum-scores were associated with cardiometabolic, immune, and psychiatric disorders.

Tangential growth of the human cerebral cortex is driven by cell proliferation during the first and second trimester of pregnancy. Fetal growth peaks in mid-gestation. Here, we explore how genes associated with fetal growth relate to cortical growth. We find that both maternal and fetal genetic variants associated with higher birthweight predict larger cortical surface area. The relative dominance of the maternal vs . fetal variants in these associations show striking variations across birth years (1943 to 1966). The birth-year patterns vary as a function of the epigenetic status near genes differentially methylated in individuals exposed (or not) to famine during the Dutch Winter of 1944/1945. Thus, it appears that the two sets of molecular processes contribute to early cortical development to a different degree in times of food scarcity or its abundance. The human cerebral cortex grows the fastest before birth. Here, the authors find positive associations between cortical expansion and both maternal and fetal birthweight genetics, and that the effects vary across years of birth.

Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more data points than subjects, in addition to multicollinearity and low signal-to-noise. Consequently, the relative efficacy of different machine learning regression algorithms for different types of neuroimaging data are not known. Here, we sought to quantify the performance of a variety of machine learning algorithms for use with neuroimaging data with various sample sizes, feature set sizes, and predictor effect sizes. The contribution of additional machine learning techniques – embedded feature selection and bootstrap aggregation (bagging) – to model performance was also quantified. Five machine learning regression methods – Gaussian Process Regression, Multiple Kernel Learning, Kernel Ridge Regression, the Elastic Net and Random Forest, were examined with both real and simulated MRI data, and in comparison to standard multiple regression. The different machine learning regression algorithms produced varying results, which depended on sample size, feature set size, and predictor effect size. When the effect size was large, the Elastic Net, Kernel Ridge Regression and Gaussian Process Regression performed well at most sample sizes and feature set sizes. However, when the effect size was small, only the Elastic Net made accurate predictions, but this was limited to analyses with sample sizes greater than 400. Random Forest also produced a moderate performance for small effect sizes, but could do so across all sample sizes. Machine learning techniques also improved prediction accuracy for multiple regression. These data provide empirical evidence for the differential performance of various machines on neuroimaging data, which are dependent on number of sample size, features and effect size. •The choice of machine learning algorithm influenced prediction accuracy.•Sample size was important: prediction accuracy generally increased once N ≥ 400.•The Elastic Net performed well at a range of effect sizes, relative to other methods.•Random Forest performed well at small effect sizes.•Gaussian Process Regression performed well at large effect sizes.