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.

Objectives Determine if epigenetic markers predict dimensional ratings of depression in maltreated children. Method A Genome-wide methylation study was completed using the Illumina 450K BeadChip array in 94 maltreated and 96 non-traumatized children with saliva-derived DNA. The 450K BeadChip does not include any methylation sites in the exact location as sites in candidate genes previously examined in the literature, so a test for replication of prior research findings was not feasible. Results Methylation in three genes emerged as genomewide-significant predictors of depression: DNA-Binding Protein Inhibitor ID-3 (ID3); Glutamate Receptor, Ionotropic NMDA 1 (GRIN1); and Tubulin Polymerization Promoting Protein (TPPP) (p<5.0 × 10−7, all analyses). These genes are all biologically relevant–with ID3 involved in the stress response, GRIN1 involved in neural plasticity, and TPPP involved in neural circuitry development. Methylation in CpG sites in candidate genes were not predictors of depression at significance levels corrected for whole genome testing, but maltreated and control children did have significantly different beta values after Bonferroni correction at multiple methylation sites in these candidate genes (e.g., BDNF, NR3C1, FKBP5). Conclusion This study suggests epigenetic changes in ID3, GRIN1, and TPPP genes, in combination with experiences of maltreatment, may confer risk for depression in children. It adds to a growing body of literature supporting a role for epigenetic mechanisms in the pathophysiology of stress-related psychiatric disorders. While epigenetic changes are frequently long lasting, they are not necessarily permanent. Consequently, interventions to reverse the negative biological and behavioral sequelae associated with child maltreatment are briefly discussed.

Contrary to expectations derived from preclinical studies of the effects of stress, and imaging studies of adults with PTSD, there is no evidence of hippocampus atrophy in children with PTSD. Multiple pediatric studies have reported reductions in the corpus callosum -the primary white matter tract in the brain. Consequently, in the present study, Diffusion Tensor Imaging was used to assess corpus callosum white matter integrity in 17 maltreated children with PTSD and 15 demographically matched normal controls. Children with PTSD had reduced fractional anisotropy in the medial and posterior corpus, a region which contains interhemispheric projections from brain structures involved in circuits that mediate the processing of emotional stimuli and various memory functions ---core disturbances associated with a history of trauma. Further exploration of the effects of stress on corpus callosum and white matter development appears a promising strategy to better understanding the pathophysiology of PTSD in children.

The objective of this study is to present the rationale, methods, design and preliminary results from the High Risk Cohort Study for the Development of Childhood Psychiatric Disorders. We describe the sample selection and the components of each phases of the study, its instruments, tasks and procedures. Preliminary results are limited to the baseline phase and encompass: (i) the efficacy of the oversampling procedure used to increase the frequency of both child and family psychopathology; (ii) interrater reliability and (iii) the role of differential participation rate. A total of 9937 children from 57 schools participated in the screening procedures. From those 2512 (random = 958; high risk = 1554) were further evaluated with diagnostic instruments. The prevalence of any child mental disorder in the random strata and high-risk strata was 19.9% and 29.7%. The oversampling procedure was successful in selecting a sample with higher family rates of any mental disorders according to diagnostic instruments. Interrater reliability (kappa) for the main diagnostic instrument range from 0.72 (hyperkinetic disorders) to 0.84 (emotional disorders). The screening instrument was successful in selecting a sub-sample with "high risk" for developing mental disorders. This study may help advance the field of child psychiatry and ultimately provide useful clinical information.

Objective: The objective of this update article is to report structural and functional neuroimaging studies exploring the potential role of cerebellum in the pathophysiology of psychiatric disorders. Method: A non-systematic literature review was conducted by means of Medline using the following terms as a parameter : "cerebellum", "cerebellar vermis", "schizophrenia", "bipolar disorder", "depression", "anxiety disorders", "dementia" and "attention

Male bonnet monkeys (Macaca radiata) were subjected to the Variable Foraging Demand (VFD) early stress paradigm as infants, MRI scans were completed an average of four years later, and behavioral assessments of anxiety and ex-vivo corpus callosum (CC) measurements were made when animals were fully matured. VFD rearing was associated with smaller CC size, CC measurements were found to correlate with fearful behavior in adulthood, and ex-vivo CC assessments showed high consistency with earlier MRI measures. Region of Interest (ROI) hippocampus and whole brain voxel- based morphometry assessments were also completed and VFD rearing was associated with reduced hippocampus and inferior and middle temporal gyri volumes. Animals were also characterized according to serotonin transporter genotype (5-HTTLPR), and the effect of genotype on imaging parameters was explored. The current findings highlight the importance of future research to better understand the effects of stress on brain development in multiple regions, including the corpus callosum, hippocampus, and other regions involved in emotion processing. Nonhuman primates provide a powerful model to unravel the mechanisms by which early stress and genetic makeup interact to produce long-term changes in brain development, stress reactivity, and risk for psychiatric disorders.

Neuroimaging-based models contribute to increasing our understanding of schizophrenia pathophysiology and can reveal the underlying characteristics of this and other clinical conditions. However, the considerable variability in reported neuroimaging results mirrors the heterogeneity of the disorder. Machine learning methods capable of representing invariant features could circumvent this problem. In this structural MRI study, we trained a deep learning model known as deep belief network (DBN) to extract features from brain morphometry data and investigated its performance in discriminating between healthy controls (N = 83) and patients with schizophrenia (N = 143). We further analysed performance in classifying patients with a first-episode psychosis (N = 32). The DBN highlighted differences between classes, especially in the frontal, temporal, parietal, and insular cortices, and in some subcortical regions, including the corpus callosum, putamen, and cerebellum. The DBN was slightly more accurate as a classifier (accuracy = 73.6%) than the support vector machine (accuracy = 68.1%). Finally, the error rate of the DBN in classifying first-episode patients was 56.3%, indicating that the representations learned from patients with schizophrenia and healthy controls were not suitable to define these patients. Our data suggest that deep learning could improve our understanding of psychiatric disorders such as schizophrenia by improving neuromorphometric analyses.

Abstract. In this work, we presentam ethodf or the integration of feature and intensityi nformation for non rigid registration. Our method is based on af ree-form deformation model, and uses an ormalized mutual information intensitys imilaritym etric to matchi ntensities and the robust pointm atching framework to estimate feature (point) correspondences. The intensitya nd feature components of the registration are posed in as ingle energy functional with associated weights. We compare our methodt ob oth point-based and intensity-based registrations. In particular, we evaluate registration accuracy as measured by point landmark distances and image intensitys imilarityo nas et of seventeen normal subjects. These results suggest that the integration of intensity and point-based registration is highly effectivei ny ielding more accurate registrations. 1I ntroductionNon rigid image registration is ac entral task in medical image analysis. In the particular case of the brain, there are an umbero fi mportanta pplications including comparing shapea nd function between individuals or groups, developing probabilistic models and atlases, measuring change within an individual and determining location with respect to ap reacquired image during stereotactic surgery.T he detailed comparison and nonrigid registration of brain images requires the determination of correspondence throughout the brain and the transformation of the image space according to this correspondence. In addition, a large number of other image analysis problems can in fact be posed as non rigid registration problems sucha ss egmentation (via the use of an atlas), motiontracking, etc.There have been manya pproaches recently to nonrigid registration, with a particular emphasis on applications to brain imaging (see the collection [15]). Most commonly,n on-linear registration methods use image intensities to compute the transformation (e.g. [2,7,14,13,8].) These techniques are potentially highly accurate but can be susceptible to local minima. In particular, the high anatomic variabilityofthe cortex often results in intensitybased methods yielding inaccurate results. Feature based and integrated feature-intensitym ethods