Low-dimensional controllability of brain networks

Network controllability is a powerful tool to study causal relationships in complex systems and identify the driver nodes for steering the network dynamics into desired states. However, due to ill-posed conditions, results become unreliable when the number of drivers becomes too small compared to the network size. This is a very common situation, particularly in real-world applications, where the possibility to access multiple nodes at the same time is limited by technological constraints, such as in the human brain. Although targeting smaller network parts might improve accuracy, challenges may remain for extremely unbalanced situations, when for example there is one single driver. To address this problem, we developed a mathematical framework that combines concepts from spectral graph theory and modern network science. Instead of controlling the original network dynamics, we aimed to control its low-dimensional embedding into the topological space derived from the network Laplacian. By performing extensive simulations on synthetic networks, we showed that a relatively low number of projected components is enough to improve the overall control accuracy, notably when dealing with very few drivers. Based on these findings, we introduced alternative low-dimensional controllability metrics and used them to identify the main driver areas of the human connectome obtained from N=6134 healthy individuals in the UK-biobank cohort. Results revealed previously unappreciated influential regions compared to standard approaches, enabled to draw control maps between distinct specialized large-scale brain systems, and yielded an anatomically-based understanding of hemispheric functional lateralization. Taken together, our results offered a theoretically-grounded solution to deal with network controllability in real-life applications and provided insights into the causal interactions of the human brain

Linear Mixed Models Minimise False Positive Rate and Enhance Precision of Mass Univariate Vertex-Wise Analyse of Grey-Matter

International audienceWe evaluated the statistical power, family wise error rate (FWER) and precision of several competing methods that perform mass-univariate vertex-wise analyses of grey-matter (thickness and surface area). In particular, we compared several generalised linear models (GLMs, current state of the art) to linear mixed models (LMMs) that have proven superior in genomics. We used phenotypes simulated from real vertex-wise data and a large sample size (N=8,662) which may soon become the norm in neuroimaging. No method ensured a FWER<5% (at a vertex or cluster level) after applying Bonferroni correction for multiple testing. LMMs should be preferred to GLMs as they minimise the false positive rate and yield smaller clusters of associations. Associations on real phenotypes must be interpreted with caution, and replication may be warranted to conclude about an association

A comparison between early presentation of dementia with Lewy Bodies, Alzheimer's disease and Parkinson's disease: evidence from routine primary care and UK Biobank data

OBJECTIVE: To simultaneously contrast prediagnostic clinical characteristics of individuals with a final diagnosis of dementia with Lewy Bodies, Parkinson's disease, Alzheimer's disease compared to controls without neurodegenerative disorders. METHODS: Using the longitudinal THIN database in the UK, we tested the association of each neurodegenerative disorder with a selected list of symptoms and broad families of treatments, and compared the associations between disorders to detect disease-specific effects. We replicated the main findings in the UK Biobank. RESULTS: We used data of 28,222 patients with PD, 20,214 with AD, 4,682 with DLB and 20,214 controls. All neurodegenerative disorders were significantly associated with the presence of multiple clinical characteristics before their diagnosis including sleep disorders, falls, psychiatric symptoms and autonomic dysfunctions. When comparing DLB patients with patients with PD and AD patients, falls, psychiatric symptoms and autonomic dysfunction were all more strongly associated with DLB in the five years preceding the first neurodegenerative diagnosis. The use of statins was lower in patients who developed PD and higher in patients who developed DLB compared to AD. In PD patients, the use of statins was associated with the development of dementia in the five years following PD diagnosis. INTERPRETATION: Prediagnostic presentations of falls, psychiatric symptoms and autonomic dysfunctions were more strongly associated with DLB than PD and AD. This study also suggests that whilst several associations with medications are similar in neurodegenerative disorders, statin usage is negatively associated with Parkinson's Disease but positively with DLB and AD as well as development of dementia in PD

Are sex differences in human brain structure associated with sex differences in behavior?

On average, men and women differ in brain structure and behaviour, raising the possibility of a link between sex differences in brain and behaviour. But women and men are also subject to different societal and cultural norms. We navigated this challenge by investigating variability of sex-differentiated brain structure within each sex. Using data from the Queensland Twin IMaging study (N=1,040) and Human Connectome Project (N=1,113), we obtained data-driven measures of individual differences along a male-female dimension for brain and behaviour based on average sex differences in brain structure and behaviour, respectively. We found a weak association between these brain and behavioural differences, driven by brain size. These brain and behavioural differences were moderately heritable. Our findings suggest that behavioural sex differences are to some extent related to sex differences in brain structure, but that this is mainly driven by differences in brain size, and causality should be interpreted cautiously

Are sex differences in human brain structure associated with sex differences in behaviour?

On average, men and women differ in brain structure and behaviour, raising the possibility of a link between sex differences in brain and behaviour. But women and men are also subject to different societal and cultural norms. We navigated this challenge by investigating variability of sex-differentiated brain structure within each sex. Using data from the Queensland Twin IMaging study (N=1,040) and Human Connectome Project (N=1,113), we obtained data-driven measures of individual differences along a male-female dimension for brain and behaviour based on average sex differences in brain structure and behaviour, respectively. We found a weak association between these brain and behavioural differences, driven by brain size. These brain and behavioural differences were moderately heritable. Our findings suggest that behavioural sex differences are to some extent related to sex differences in brain structure, but that this is mainly driven by differences in brain size, and causality should be interpreted cautiously

Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture

© 2020, The Author(s). Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer’s disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P-threshold (Poptimal) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and individuals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD

DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible