The German E-Health Act became effective in January 2016. It promotes the use of information and communication technology within the German healthcare system. It defines the uniform framework for future cross-sectoral network­ ing and cooperation between healthcare providers and the parties paying for healthcare services. The German Federal Ministry of Health is responsible for the design of the legal framework of the telematics infrastructure. The German Society for Telematics (gematik) is responsible for the introduction of the telematics infrastructure and the Electronic Health Card. The Electronic Health Card allows digital storage and retrieval of patient data. Its applications will be in­ troduced gradually.

[Digital health: A paradigm shift in clinical medicine?].

Common risk factors for psychiatric and other brain disorders are likely to converge on biological pathways influencing the development and maintenance of brain structure and function across life. Using structural MRI data from 45,615 individuals aged 3-96 years, we demonstrate distinct patterns of apparent brain aging in several brain disorders and reveal genetic pleiotropy between apparent brain aging in healthy individuals and common brain disorders.

/pdf/common-brain-disorders-are-associated-with-heritable-27dqjcmve1.pdf

Common brain disorders are associated with heritable patterns of apparent aging of the brain

The protracted development of structural and functional brain connectivity within distributed association networks coincides with improvements in higher-order cognitive processes such as executive function. However, it remains unclear how white-matter architecture develops during youth to directly support coordinated neural activity. Here, we characterize the development of structure–function coupling using diffusion-weighted imaging and n-back functional MRI data in a sample of 727 individuals (ages 8 to 23 y). We found that spatial variability in structure–function coupling aligned with cortical hierarchies of functional specialization and evolutionary expansion. Furthermore, hierarchy-dependent age effects on structure–function coupling localized to transmodal cortex in both cross-sectional data and a subset of participants with longitudinal data (n = 294). Moreover, structure–function coupling in rostrolateral prefrontal cortex was associated with executive performance and partially mediated age-related improvements in executive function. Together, these findings delineate a critical dimension of adolescent brain development, whereby the coupling between structural and functional connectivity remodels to support functional specialization and cognition.

https://www.pnas.org/content/pnas/117/1/771.full.pdf

Development of structure–function coupling in human brain networks during youth

Motivated by prior data on local cortical shrinkage and intracortical myelination, we predicted age-related changes in topological organization of cortical structural networks during adolescence. We estimated structural correlation from magnetic resonance imaging measures of cortical thickness at 308 regions in a sample of N = 297 healthy participants, aged 14-24 years. We used a novel sliding-window analysis to measure age-related changes in network attributes globally, locally and in the context of several community partitions of the network. We found that the strength of structural correlation generally decreased as a function of age. Association cortical regions demonstrated a sharp decrease in nodal degree (hubness) from 14 years, reaching a minimum at approximately 19 years, and then levelling off or even slightly increasing until 24 years. Greater and more prolonged age-related changes in degree of cortical regions within the brain network were associated with faster rates of adolescent cortical myelination and shrinkage. The brain regions that demonstrated the greatest age-related changes were concentrated within prefrontal modules. We conclude that human adolescence is associated with biologically plausible changes in structural imaging markers of brain network organization, consistent with the concept of tuning or consolidating anatomical connectivity between frontal cortex and the rest of the connectome.

/pdf/adolescent-tuning-of-association-cortex-in-human-structural-5eb65byhlg.pdf

Adolescent Tuning of Association Cortex in Human Structural Brain Networks.

Schizophrenia has been conceived as a disorder of brain connectivity, but it is unclear how this network phenotype is related to the underlying genetics. We used morphometric similarity analysis of MRI data as a marker of interareal cortical connectivity in three prior case–control studies of psychosis: in total, n = 185 cases and n = 227 controls. Psychosis was associated with globally reduced morphometric similarity in all three studies. There was also a replicable pattern of case–control differences in regional morphometric similarity, which was significantly reduced in patients in frontal and temporal cortical areas but increased in parietal cortex. Using prior brain-wide gene expression data, we found that the cortical map of case–control differences in morphometric similarity was spatially correlated with cortical expression of a weighted combination of genes enriched for neurobiologically relevant ontology terms and pathways. In addition, genes that were normally overexpressed in cortical areas with reduced morphometric similarity were significantly up-regulated in three prior post mortem studies of schizophrenia. We propose that this combined analysis of neuroimaging and transcriptional data provides insight into how previously implicated genes and proteins as well as a number of unreported genes in their topological vicinity on the protein interaction network may drive structural brain network changes mediating the genetic risk of schizophrenia.

https://www.pnas.org/content/pnas/116/19/9604.full.pdf

Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes.

Quantitative assessment of structural image quality.

Data quality is increasingly recognized as one of the most important confounders in brain imaging research. It is particularly important for studies of brain development, where age is systematically related to in-scanner motion and data quality. Prior work has demonstrated that in-scanner head motion biases estimates of structural neuroimaging measures. Yet, objective measures of data quality are not available for most structural brain images. Here we sought to identify reliable, quantitative measures of data quality for T1-weighted volumes, describe how such measures of quality relate to common measures of brain structure, and delineate how this in turn may bias inference regarding brain development in youth. Three highly-trained raters provided manual ratings for 1601 T1-weighted volumes acquired as part of the Philadelphia Neurodevelopmental Cohort. Expert manual ratings were compared to automated quality measures, which derived measures from the Preprocessed Connectomes Project9s Quality Assurance Protocol (QAP). Generalized linear mixed-effects models using the automated quality measures were constructed in a training sample (n = 1067) to: 1) identify unusable images with significant artifacts, and 2) quantify subtle artifacts in usable images. These models were then tested in an independent validation dataset (n = 534). Results reveal that unusable images can be detected with a high degree of accuracy: a model including background kurtosis and skewness achieved an AUC of 0.95 in the training dataset and 0.94 in the independent validation dataset. While identification of subtle artifact was more challenging, an 8-parameter model achieved an AUC of 0.80 in the training dataset, and 0.92 in the validation dataset. Notably, quantitative measures of image quality were related to cortical thickness and gray matter density; measures of cortical volume were less affected by artifact. Furthermore, these quantitative measures of image quality demonstrated comparable or superior performance to estimates of motion derived from other imaging sequences acquired during the same protocol. Finally, data quality significantly altered structural brain maturation occurring during adolescent development. Taken together, these results indicate that reliable measures of data quality can be automatically derived from T1-weighted volumes, and that failing to control for data quality can systematically bias the results of studies of brain development.

/pdf/data-driven-assessment-of-structural-image-quality-1f53jmijst.pdf

Data-driven Assessment of Structural Image Quality

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Quantitative assessment of structural image quality.

Data-driven Assessment of Structural Image Quality