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.

BackgroundAlthough it is well recognized that autism is associated with altered patterns of over- and under-connectivity, specifics are still a matter of debate. Little has been done so far to synthesize available literature using whole-brain electroencephalography (EEG) and magnetoencephalography (MEG) recordings.Objectives1) To systematically review the literature on EEG/MEG functional and effective connectivity in autism spectrum disorder (ASD), 2) to synthesize and critically appraise findings related with the hypothesis that ASD is characterized by long-range underconnectivity and local overconnectivity, and 3) to provide, based on the literature, an analysis of tentative factors that are likely to mediate association between ASD and atypical connectivity (e.g., development, topography, lateralization).MethodsLiterature reviews were done using PubMed and PsychInfo databases. Abstracts were screened, and only relevant articles were analyzed based on the objectives of this paper. Special attention was paid to the methodological characteristics that could have created variability in outcomes reported between studies.ResultsOur synthesis provides relatively strong support for long-range underconnectivity in ASD, whereas the status of local connectivity remains unclear. This observation was also mirrored by a similar relationship with lower frequencies being often associated with underconnectivity and higher frequencies being associated with both under- and over-connectivity. Putting together these observations, we propose that ASD is characterized by a general trend toward an under-expression of lower-band wide-spread integrative processes compensated by more focal, higher-frequency, locally specialized, and segregated processes. Further investigation is, however, needed to corroborate the conclusion and its generalizability across different tasks. Of note, abnormal lateralization in ASD, specifically an elevated left-over-right EEG and MEG functional connectivity ratio, has been also reported consistently across studies.ConclusionsThe large variability in study samples and methodology makes a systematic quantitative analysis (i.e. meta-analysis) of this body of research impossible. Nevertheless, a general trend supporting the hypothesis of long-range functional underconnectivity can be observed. Further research is necessary to more confidently determine the status of the hypothesis of short-range overconnectivity. Frequency-band specific patterns and their relationships with known symptoms of autism also need to be further clarified.

Recent findings from developmental neuroimaging studies suggest that the enhancement of cognitive processes during development may be the result of a fine-tuning of the structural and functional organization of brain with maturation. However, the details regarding the developmental trajectory of large-scale structural brain networks are not yet understood. Here, we used graph theory to examine developmental changes in the organization of structural brain networks in 203 normally growing children and adolescents. Structural brain networks were constructed using interregional correlations in cortical thickness for 4 age groups (early childhood: 4.8-8.4 year; late childhood: 8.5-11.3 year; early adolescence: 11.4-14.7 year; late adolescence: 14.8-18.3 year). Late childhood showed prominent changes in topological properties, specifically a significant reduction in local efficiency, modularity, and increased global efficiency, suggesting a shift of topological organization toward a more random configuration. An increase in number and span of distribution of connector hubs was found in this age group. Finally, inter-regional connectivity analysis and graph-theoretic measures indicated early maturation of primary sensorimotor regions and protracted development of higher order association and paralimbic regions. Our finding reveals a time window of plasticity occurring during late childhood which may accommodate crucial changes during puberty and the new developmental tasks that an adolescent faces.

Neuroimaging studies in autism spectrum disorders (ASDs) have provided inconsistent evidence of cortical abnormality. This is probably due to the small sample sizes used in most studies, and important differences in sample characteristics, particularly age, as well as to the heterogeneity of the disorder. To address these issues, we assessed abnormalities in ASD within the Autism Brain Imaging Data Exchange data set, which comprises data from approximately 1100 individuals (~6-55 years). A subset of these data that met stringent quality control and inclusion criteria (560 male subjects; 266 ASD; age = 6-35 years) were used to compute age-specific differences in cortical thickness in ASD and the relationship of any such differences to symptom severity of ASD. Our results show widespread increased cortical thickness in ASD, primarily left lateralized, from 6 years onwards, with differences diminishing during adulthood. The severity of symptoms related to social affect and communication correlated with these cortical abnormalities. These results are consistent with the conjecture that developmental patterns of cortical thickness abnormalities reflect delayed cortical maturation and highlight the dynamic nature of morphological abnormalities in ASD.

Initiating joint attention (IJA), the behavioral instigation of coordinated focus of 2 people on an object, emerges over the first 2 years of life and supports social-communicative functioning related to the healthy development of aspects of language, empathy, and theory of mind. Deficits in IJA provide strong early indicators for autism spectrum disorder, and therapies targeting joint attention have shown tremendous promise. However, the brain systems underlying IJA in early childhood are poorly understood, due in part to significant methodological challenges in imaging localized brain function that supports social behaviors during the first 2 years of life. Herein, we show that the functional organization of the brain is intimately related to the emergence of IJA using functional connectivity magnetic resonance imaging and dimensional behavioral assessments in a large semilongitudinal cohort of infants and toddlers. In particular, though functional connections spanning the brain are involved in IJA, the strongest brain-behavior associations cluster within connections between a small subset of functional brain networks; namely between the visual network and dorsal attention network and between the visual network and posterior cingulate aspects of the default mode network. These observations mark the earliest known description of how functional brain systems underlie a burgeoning fundamental social behavior, may help improve the design of targeted therapies for neurodevelopmental disorders, and, more generally, elucidate physiological mechanisms essential to healthy social behavior development.

Numerous brain imaging studies indicate that the corpus callosum is smaller in older children and adults with autism spectrum disorder. However, there are no published studies examining the morphological development of this connective pathway in infants at-risk for the disorder. Magnetic resonance imaging data were collected from 270 infants at high familial risk for autism spectrum disorder and 108 low-risk controls at 6, 12 and 24 months of age, with 83% of infants contributing two or more data points. Fifty-seven children met criteria for ASD based on clinical-best estimate diagnosis at age 2 years. Corpora callosa were measured for area, length and thickness by automated segmentation. We found significantly increased corpus callosum area and thickness in children with autism spectrum disorder starting at 6 months of age. These differences were particularly robust in the anterior corpus callosum at the 6 and 12 month time points. Regression analysis indicated that radial diffusivity in this region, measured by diffusion tensor imaging, inversely predicted thickness. Measures of area and thickness in the first year of life were correlated with repetitive behaviours at age 2 years. In contrast to work from older children and adults, our findings suggest that the corpus callosum may be larger in infants who go on to develop autism spectrum disorder. This result was apparent with or without adjustment for total brain volume. Although we did not see a significant interaction between group and age, cross-sectional data indicated that area and thickness differences diminish by age 2 years. Regression data incorporating diffusion tensor imaging suggest that microstructural properties of callosal white matter, which includes myelination and axon composition, may explain group differences in morphology.

Associations between brain cortical tissue volume and cognitive function in old age are frequently interpreted as suggesting that preservation of cortical tissue is the foundation of successful cognitive aging. However, this association could also, in part, reflect a lifelong association between cognitive ability and cortical tissue. We analyzed data on 588 subjects from the Lothian Birth Cohort 1936 who had intelligence quotient (IQ) scores from the same cognitive test available at both 11 and 70 years of age as well as high-resolution brain magnetic resonance imaging data obtained at approximately 73 years of age. Cortical thickness was estimated at 81 924 sampling points across the cortex for each subject using an automated pipeline. Multiple regression was used to assess associations between cortical thickness and the IQ measures at 11 and 70 years. Childhood IQ accounted for more than two-third of the association between IQ at 70 years and cortical thickness measured at age 73 years. This warns against ascribing a causal interpretation to the association between cognitive ability and cortical tissue in old age based on assumptions about, and exclusive reference to, the aging process and any associated disease. Without early-life measures of cognitive ability, it would have been tempting to conclude that preservation of cortical thickness in old age is a foundation for successful cognitive aging when, instead, it is a lifelong association. This being said, results should not be construed as meaning that all studies on aging require direct measures of childhood IQ, but as suggesting that proxy measures of prior cognitive function can be useful to take into consideration.

Autism spectrum disorder (ASD) is a developmental disorder defined by behavioral symptoms that emerge during the first years of life. Associated with these symptoms are differences in the structure of a wide array of brain regions, and in the connectivity between these regions. However, the use of cohorts with large age variability and participants past the generally recognized age of onset of the defining behaviors means that many of the reported abnormalities may be a result of cascade effects of developmentally earlier deviations. This study assessed differences in connectivity in ASD at the age at which the defining behaviors first become clear. There were 113 24-month-old participants at high risk for ASD, 31 of whom were classified as ASD, and 23 typically developing 24-month-old participants at low risk for ASD. Utilizing diffusion data to obtain measures of the length and strength of connections between anatomical regions, we performed an analysis of network efficiency. Our results showed significantly decreased local and global efficiency over temporal, parietal and occipital lobes in high-risk infants classified as ASD, relative to both low- and high-risk infants not classified as ASD. The frontal lobes showed only a reduction in global efficiency in Broca's area. In addition, these same regions showed an inverse relation between efficiency and symptom severity across the high-risk infants. The results suggest delay or deficits in infants with ASD in the optimization of both local and global aspects of network structure in regions involved in processing auditory and visual stimuli, language and nonlinguistic social stimuli.