Effects of tractography filtering on the topology and interpretability of connectomes

International audienceThe analysis of connectomes and their associated network metrics forms an important part of clinical studies. These connectomes are based on tractography algorithms to estimate the structural connectivity between brain regions. However, tractography algorithms, are prone to false positive connections and this affects the quality of the connectomes. Several tractography filtering techniques (TFTs) have been proposed to alleviate this issue in studies, but their effect on connectomic analyses of pathology has not been investigated. The aim of our work is to investigate how TFTs affect network metrics and their interpretation in the context of clinical studies

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

How do spatially distinct frequency specific MEG networks emerge from one underlying structural connectome? The role of the structural eigenmodes

Functional networks obtained from magnetoencephalography (MEG) from different frequency bands show distinct spatial patterns. It remains to be elucidated how distinct spatial patterns in MEG networks emerge given a single underlying structural network. Recent work has suggested that the eigenmodes of the structural network might serve as a basis set for functional network patterns in the case of functional MRI. Here, we take this notion further in the context of frequency band specific MEG networks. We show that a selected set of eigenmodes of the structural network can predict different frequency band specific networks in the resting state, ranging from delta (1–4 Hz) to the high gamma band (40–70 Hz). These predictions outperform predictions based from surrogate data, suggesting a genuine relationship between eigenmodes of the structural network and frequency specific MEG networks. We then show that the relevant set of eigenmodes can be excited in a network of neural mass models using linear stability analysis only by including delays. Excitation of an eigenmode in this context refers to a dynamic instability of a network steady state to a spatial pattern with a corresponding coherent temporal oscillation. Simulations verify the results from linear stability analysis and suggest that theta, alpha and beta band networks emerge very near to the bifurcation. The delta and gamma bands in the resting state emerges further away from the bifurcation. These results show for the first time how delayed interactions can excite the relevant set of eigenmodes that give rise to frequency specific functional connectivity patterns

Communicability distance reveals hidden patterns of alzheimer’s disease

The communicability distance between pairs of regions in human brain is used as a quantitative proxy for studying Alzheimer’s disease. Using this distance, we obtain the shortest communicability path lengths between different regions of brain networks from patients with Alzheimer’s disease (AD) and healthy cohorts (HC). We show that the shortest communicability path length is significantly better than the shortest topological path length in distinguishing AD patients from HC. Based on this approach, we identify 399 pairs of brain regions for which there are very significant changes in the shortest communicability path length after AD appears. We find that 42% of these regions interconnect both brain hemispheres, 28% connect regions inside the left hemisphere only, and 20% affect vermis connection with brain hemispheres. These findings clearly agree with the disconnection syndrome hypothesis of AD. Finally, we show that in 76.9% of damaged brain regions the shortest communicability path length drops in AD in relation to HC. This counterintuitive finding indicates that AD transforms the brain network into a more efficient system from the perspective of the transmission of the disease, because it drops the circulability of the disease factor around the brain regions in relation to its transmissibility to other regions

brainlife.io: a decentralized and open-source cloud platform to support neuroscience research

Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants

Investigating structural network disruption in multiple sclerosis

Multiple sclerosis (MS) is an inflammatory, demyelinating and neurodegenerative disease of the central nervous system (CNS). Conventional whole brain magnetic resonance imaging (MRI) measures do not sufficiently explain disability in MS. Network science provides a powerful approach to study brain organizational principles and in combination with graph theory has revealed fundamental connectivity patterns in neurological conditions including MS. The overarching aim of this thesis is to investigate structural network disruption in MS evaluating the potential of brain networks analyses as novel biomarkers in MS pathology. The results of this thesis add to the current scientific knowledge. In particular, by applying an optimised structural network reconstruction pipeline we demonstrated that network metrics explain disability better in MS over and above conventional non- network metrics. In addition, in the absence of any longitudinal network studies, we developed a longitudinal network pipeline which we then applied to our longitudinal data. These findings demonstrated for the first time that baseline structural network metrics are predictors of future deep grey matter atrophy and increased lesion load. Finally, we applied a data-driven network decomposition approach detecting progressive weakening of connections that is linked to the severity of MS subtypes suggesting that these techniques are sensitive to pathology. The results presented here highlight the potential of network-based approaches as complementary methods for disease biomarkers to better predict disease course and monitor treatment effects. We believe that these findings may provide a framework for future studies with the aim to bridge the gap between imaging and symptomatology

Dynamical consequences of regional heterogeneity in the brain’s transcriptional landscape

Brain regions vary in their molecular and cellular composition, but how this heterogeneity shapes neuronal dynamics is unclear. Here, we investigate the dynamical consequences of regional heterogeneity using a biophysical model of whole-brain functional magnetic resonance imaging (MRI) dynamics in humans. We show that models in which transcriptional variations in excitatory and inhibitory receptor (E:I) gene expression constrain regional heterogeneity more accurately reproduce the spatiotemporal structure of empirical functional connectivity estimates than do models constrained by global gene expression profiles or MRI-derived estimates of myeloarchitecture. We further show that regional transcriptional heterogeneity is essential for yielding both ignition-like dynamics, which are thought to support conscious processing, and a wide variance of regional-activity time scales, which supports a broad dynamical range. We thus identify a key role for E:I heterogeneity in generating complex neuronal dynamics and demonstrate the viability of using transcriptomic data to constrain models of large-scale brain function

Investigating white matter changes underlying overactive bladder in multiple sclerosis with diffusion MRI

Lower urinary tract symptoms (LUTS) are presented in more than 80% of multiple sclerosis (MS) patients. Current understanding of LUT control is based on studies exploring activities in grey matter (GM) and investigating functional correlations with LUTS. The relationship between white matter (WM) changes and overactive bladder (OAB) symptoms are limited to findings in small vessel disease, and the nature of the association between WM changes and OAB symptoms is poorly understood. Advanced diffusion-weighted magnetic resonance imaging (MRI) techniques provide non-invasive techniques to study WM abnormalities and correlates to clinical observations. The overarching objectives of this work are to explore WM abnormalities subtending OAB symptoms in MS, and to reconstruct the structural network underpinning the working model of lower urinary tract (LUT) control. Using Tract-Based Spatial Statistics (TBSS), OAB symptoms related WM abnormalities in MS can be identified, and a structural network subtending OAB symptoms in MS can be subsequently created. The findings of this work illustrate the correlation between OAB symptoms severity and WM abnormalities in MS. These were observed in regions in frontal lobes and non-dominant hemisphere, including corpus callosum, anterior corona radiata bilaterally, right anterior thalamic radiation, superior longitudinal fasciculus bilaterally, and right inferior longitudinal fasciculus. The structural network created for OAB symptoms in MS connected regions known to be involved in the working model of LUT control, and the network identified connectivity between insula and frontal lobe, which is the key circuit for perception of bladder fullness. Moreover, structural connectivity between insula-temporal lobe and insula-occipital lobe were observed, which may underpin changes seen in functional MRI (fMRI) studies. The novel findings of this study present WM abnormalities and structural connectivity subtending LUTS in MS with diffusion-weighted imaging (DWI). The techniques used in this work can be applied to other patterns of LUTS and other neurological diseases

Classification and early detection of dementia and cognitive decline with magnetic resonance imaging

Dementie is een verwoestende ziekte waar wereldwijd miljoenen mensen aan leiden. De meest voorkomende oorzaak van dementie is de ziekte van Alzheimer. Voor het ontwikkelen van effectieve behandelingen is het belangrijk om dementie in een vroeg stadium te detecteren. Traditioneel alzheimeronderzoek is voornamelijk gericht op groepsverschillen tussen patiënten en controles. Recent onderzoek is deels verschoven naar individuele classificatie met machine learning. In dit proefschrift onderzoeken we het gebruik van magnetic resonance imaging (MRI) voor automatische detectie van de ziekte van Alzheimer, en vroege detectie van cognitieve achteruitgang. In dit proefschrift laten we zien dat het combineren van MRI modaliteiten de classificatie kan verbeteren. Ook laten we zien dat diffusie MRI een goede maat is om alzheimer te diagnosticeren. Bij toepassing van dezelfde methoden op een groep presymptomatische gendragers die amyloïdangiopathie zullen ontwikkelen vonden we geen verschillen tussen de gendragers en controles. Tevens waren we niet in staat om cognitieve achteruitgang na 4 jaar te voorspellen in een groep ouderen met verhoogd risico op achteruitgang. Met MRI kunnen betrouwbare individuele uitspraken gedaan kan worden over patiënten, maar het is met de huidige methoden niet gevoelig voor vroege detectie van cognitieve achteruitgang.Alzheimer NederlandLUMC / Geneeskund