Bayesian reduced rank regression models generalizable neural fingerprints that differentiate between individuals in magnetoencephalography data.

Recent magnetoencephalography (MEG) studies have reported that functional connectivity (FC) and power spectra can be used as neural fingerprints in differentiating individuals. Such studies have mainly used correlations between measurement sessions to distinguish individuals from each other. However, it has remained unclear whether such correlations might reflect a more generalizable principle of individually distinctive brain patterns. Here, we evaluated a machine-learning based approach, termed latent-noise Bayesian reduced rank regression (BRRR) as a means of modelling individual differences in the resting-state MEG data of the Human Connectome Project (HCP), using FC and power spectra as neural features. First, we verified that BRRR could model and reproduce the differences between metrics that correlation-based fingerprinting yields. We trained BRRR models to distinguish individuals based on data from one measurement and used the models to identify subsequent measurement sessions of those same individuals. The best performing BRRR models, using only 20 spatiospectral components, were able to identify subjects across measurement sessions with over 90% accuracy, approaching the highest correlation-based accuracies. Using cross-validation, we then determined whether that BRRR model could generalize to unseen subjects, successfully classifying the measurement sessions of novel individuals with over 80% accuracy. The results demonstrate that individual neurofunctional differences can be reliably extracted from MEG data with a low-dimensional predictive model and that the model is able to classify novel subjects.

Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol.

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.

Analysis of the dynamic co-expression network of heart regeneration in the zebrafish.

The zebrafish has the capacity to regenerate its heart after severe injury. While the function of a few genes during this process has been studied, we are far from fully understanding how genes interact to coordinate heart regeneration. To enable systematic insights into this phenomenon, we generated and integrated a dynamic co-expression network of heart regeneration in the zebrafish and linked systems-level properties to the underlying molecular events. Across multiple post-injury time points, the network displays topological attributes of biological relevance. We show that regeneration steps are mediated by modules of transcriptionally coordinated genes, and by genes acting as network hubs. We also established direct associations between hubs and validated drivers of heart regeneration with murine and human orthologs. The resulting models and interactive analysis tools are available at http://infused.vital-it.ch. Using a worked example, we demonstrate the usefulness of this unique open resource for hypothesis generation and in silico screening for genes involved in heart regeneration.

Global functional connectivity reveals highly significant differences between the vegetative and the minimally conscious state.

A major challenge in the diagnosis of disorders of consciousness is the differential diagnosis between the vegetative state (VS) and the minimally conscious state (MCS). Clinically, VS is defined by complete unawareness, whereas MCS is defined by the presence of inconsistent but clearly discernible behavioural signs of consciousness. In healthy individuals, pain cries have been reported to elicit functional activation within the pain matrix of the brain, which may be interpreted as empathic reaction. In this study, pain cries were presented to six VS patients, six MCS patients, and 17 age-matched healthy controls. Conventional task-related functional magnetic resonance imaging (fMRI) showed no significant differences in functional activation between the VS and MCS groups. In contrast to this negative finding, the application of a novel data-driven technique for the analysis of the brain's global functional connectivity yielded a positive result. The weighted global connectivity (WGC) was significantly greater in the MCS group compared to the VS group (p < 0.05, family-wise error corrected). Using areas of significant WGC differences as 'seed regions' in a secondary connectivity analysis revealed extended functional networks in both MCS and healthy groups, whereas no such long-range functional connections were observed in the VS group. These results demonstrate the potential of functional connectivity MRI (fcMRI) as a clinical tool for differential diagnosis in disorders of consciousness.

Electroconvulsive therapy reduces frontal cortical connectivity in severe depressive disorder.

To date, electroconvulsive therapy (ECT) is the most potent treatment in severe depression. Although ECT has been successfully applied in clinical practice for over 70 years, the underlying mechanisms of action remain unclear. We used functional MRI and a unique data-driven analysis approach to examine functional connectivity in the brain before and after ECT treatment. Our results show that ECT has lasting effects on the functional architecture of the brain. A comparison of pre- and posttreatment functional connectivity data in a group of nine patients revealed a significant cluster of voxels in and around the left dorsolateral prefrontal cortical region (Brodmann areas 44, 45, and 46), where the average global functional connectivity was considerably decreased after ECT treatment (P < 0.05, family-wise error-corrected). This decrease in functional connectivity was accompanied by a significant improvement (P < 0.001) in depressive symptoms; the patients' mean scores on the Montgomery Asberg Depression Rating Scale pre- and posttreatment were 36.4 (SD = 4.9) and 10.7 (SD = 9.6), respectively. The findings reported here add weight to the emerging "hyperconnectivity hypothesis" in depression and support the proposal that increased connectivity may constitute both a biomarker for mood disorder and a potential therapeutic target.

[Chronic fatigue syndrome. More and more differential diagnoses suggest a new view of this syndrome]. / Chronic fatigue syndrome. Allt fler differentialdiagnoser kräver en förändrad syn på syndromet.

The diagnosis of chronic fatigue syndrome (CFS) requires a number of symptoms beyond chronic fatigue, according to the criteria developed in 1994 by the US Centers for Disease Control (CDC) International CFS Study Group. CFS is thus no synonym for chronic fatigue but rather an unusual syndrome afflicting no more than 0.1% of the population. Several CFS definitions have been developed over the years, and it is common for investigators to erroneously compare studies based on different definitions, which nevertheless all use the term CFS. Much of our "understanding" of CFS does not apply to the small group of patients who fulfill the current (1994) CDC definition (above). Recent studies have shown that a number of somatic diseases can present with CFS symptoms and thus be misdiagnosed as CFS. This review presents a list of such differential diagnoses, mainly chronic infections, endocrine diseases, and allergies. In view of these differential diagnoses (1) investigation and therapy must be individualized, and (2) we should offer rehabilitation where different specialists work as a coordinated team.