Population modeling with machine learning can enhance measures of mental health

Background: Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. In contrast, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention? Results: Here, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful, than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures at capturing multiple health-related constructs when modeling from, both, brain signals and sociodemographic data. Conclusion: Population modeling with machine learning can derive measures of mental health from heterogeneous inputs including brain signals and questionnaire data. This may complement or even substitute for psychometric assessments in clinical populations

A reusable benchmark of brain-age prediction from M/EEG resting-state signals

Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints

Propofol requirement and EEG alpha band power during general anesthesia provide complementary views on preoperative cognitive decline

Background: Although cognitive decline (CD) is associated with increased post-operative morbidity and mortality, routinely screening patients remains difficult. The main objective of this prospective study is to use the EEG response to a Propofol-based general anesthesia (GA) to reveal CD. Methods: 42 patients with collected EEG and Propofol target concentration infusion (TCI) during GA had a preoperative cognitive assessment using MoCA. We evaluated the performance of three variables to detect CD (MoCA < 25 points): age, Propofol requirement to induce unconsciousness (TCI at SEF95: 8–13 Hz) and the frontal alpha band power (AP at SEF95: 8–13 Hz). Results: The 17 patients (40%) with CD were significantly older (p < 0.001), had lower TCI (p < 0.001), and AP (p < 0.001). We found using logistic models that TCI and AP were the best set of variables associated with CD (AUC: 0.89) and performed better than age (p < 0.05). Propofol TCI had a greater impact on CD probability compared to AP, although both were complementary in detecting CD. Conclusion: TCI and AP contribute additively to reveal patient with preoperative cognitive decline. Further research on post-operative cognitive trajectory are necessary to confirm the interest of intra operative variables in addition or as a substitute to cognitive evaluation

Brain age as a surrogate marker for cognitive performance in multiple sclerosis

Background: Data from neuro-imaging techniques allow us to estimate a brain's age. Brain age is easily interpretable as "how old the brain looks", and could therefore be an attractive communication tool for brain health in clinical practice. This study aimed to investigate its clinical utility by investigating the relationship between brain age and cognitive performance in multiple sclerosis (MS). Methods: A linear regression model was trained to predict age from brain MRI volumetric features and sex in a healthy control dataset (HC_train, n=1673). This model was used to predict brain age in two test sets: HC_test (n=50) and MS_test (n=201). Brain-Predicted Age Difference (BPAD) was calculated as BPAD=brain age minus chronological age. Cognitive performance was assessed by the Symbol Digit Modalities Test (SDMT). Results: Brain age was significantly related to SDMT scores in the MS_test dataset (r=-0.46, p<.001), and contributed uniquely to variance in SDMT beyond chronological age, reflected by a significant correlation between BPAD and SDMT (r=-0.24, p<.001) and a significant weight (-0.25, p=0.002) in a multivariate regression equation with age. Conclusions: Brain age is a candidate biomarker for cognitive dysfunction in MS and an easy to grasp metric for brain health

A global disorder of imprinting in the human female germ line

Imprinted genes are expressed differently depending on whether they are carried by a chromosome of maternal or paternal origin. Correct imprinting is established by germline-specific modifications; failure of this process underlies several inherited human syndromes. All these imprinting control defects are cis-acting, disrupting establishment or maintenance of allele-specific epigenetic modifications across one contiguous segment of the genome. In contrast, we report here an inherited global imprinting defect. This recessive maternal-effect mutation disrupts the specification of imprints at multiple, non-contiguous loci, with the result that genes normally carrying a maternal methylation imprint assume a paternal epigenetic pattern on the maternal allele. The resulting conception is phenotypically indistinguishable from an androgenetic complete hydatidiform mole, in which abnormal extra-embryonic tissue proliferates while development of the embryo is absent or nearly so. This disorder offers a genetic route to the identification of trans-acting oocyte factors that mediate maternal imprint establishment

Comparative analysis of sequence characteristics of imprinted genes in human, mouse, and cattle

Genomic imprinting is an epigenetic mechanism that results in monoallelic expression of genes depending on parent-of-origin of the allele. Although the conservation of genomic imprinting among mammalian species has been widely reported for many genes, there is accumulating evidence that some genes escape this conservation. Most known imprinted genes have been identified in the mouse and human, with few imprinted genes reported in cattle. Comparative analysis of genomic imprinting across mammalian species would provide a powerful tool for elucidating the mechanisms regulating the unique expression of imprinted genes. In this study we analyzed the imprinting of 22 genes in human, mouse, and cattle and found that in only 11 was imprinting conserved across the three species. In addition, we analyzed the occurrence of the sequence elements CpG islands, C + G content, tandem repeats, and retrotransposable elements in imprinted and in nonimprinted (control) cattle genes. We found that imprinted genes have a higher G + C content and more CpG islands and tandem repeats. Short interspersed nuclear elements (SINEs) were notably fewer in number in imprinted cattle genes compared to control genes, which is in agreement with previous reports for human and mouse imprinted regions. Long interspersed nuclear elements (LINEs) and long terminal repeats (LTRs) were found to be significantly underrepresented in imprinted genes compared to control genes, contrary to reports on human and mouse. Of considerable significance was the finding of highly conserved tandem repeats in nine of the genes imprinted in all three species

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Роман Э. М. Форстера «Поездка в Индию» сквозь призму аналитической психологии К. Г. Юнга

По мнению большинства критиков и исследователей творчества английского прозаика Эдуарда Моргана Форстера (Edward Morgan Forster, 1879-1970), роман «Поездка в Индию» (“A Passage to India”, 1924) считается его наиболее значительным произведением, которое и по сей день вызывает многочисленные интерпретации. В силу известных причин в советском литературоведении роман изучался преимущественно в свете общественно-политической проблематики; магистральными мотивами выделялись политические и антиколониальные. Безусловно, не стоит преуменьшать социально-политическое значение романа – политическая проблематика занимает важное место в произведении, однако, вряд ли стоит ее и преувеличивать