Papers by Tommy Boshkovski
Movement Disorders, Dec 22, 2021
BackgroundEven though Parkinson's disease (PD) is typically viewed as largely affecting gray matt... more BackgroundEven though Parkinson's disease (PD) is typically viewed as largely affecting gray matter, there is growing evidence that there are also structural changes in the white matter. Traditional connectomics methods that study PD may not be specific to underlying microstructural changes, such as myelin loss.ObjectiveThe primary objective of this study is to investigate the PD‐induced changes in myelin content in the connections emerging from the basal ganglia and the brainstem. For the weighting of the connectome, we used the longitudinal relaxation rate as a biologically grounded myelin‐sensitive metric.MethodsWe computed the myelin‐weighted connectome in 35 healthy control subjects and 81 patients with PD. We used partial least squares to highlight the differences between patients with PD and healthy control subjects. Then, a ring analysis was performed on selected brainstem and subcortical regions to evaluate each node's potential role as an epicenter for disease propagation. Then, we used behavioral partial least squares to relate the myelin alterations with clinical scores.ResultsMost connections (~80%) emerging from the basal ganglia showed a reduced myelin content. The connections emerging from potential epicentral nodes (substantia nigra, nucleus basalis of Meynert, amygdala, hippocampus, and midbrain) showed significant decrease in the longitudinal relaxation rate (P < 0.05). This effect was not seen for the medulla and the pons.ConclusionsThe myelin‐weighted connectome was able to identify alteration of the myelin content in PD in basal ganglia connections. This could provide a different view on the importance of myelination in neurodegeneration and disease progression. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society
Journal of open source software, Sep 3, 2020
Magnetic resonance imaging (MRI) has revolutionized the way we look at the human body. However, c... more Magnetic resonance imaging (MRI) has revolutionized the way we look at the human body. However, conventional MR scanners are not measurement devices. They produce digital images represented by "shades of grey", and the intensity of the shades depends on the way the images are acquired. This is why it is difficult to compare images acquired at different clinical sites, limiting the diagnostic, prognostic, and scientific potential of the technology. Quantitative MRI (qMRI) aims to overcome this problem by assigning units to MR images, ensuring that the values represent a measurable quantity that can be reproduced within and across sites. While the vision for quantitative MRI is to overcome site-dependent variations, this is still a challenge due to variability in the hardware and software used by MR vendors to produce quantitative MRI maps.
bioRxiv (Cold Spring Harbor Laboratory), Aug 7, 2020
Myelin plays a crucial role in how well information travels between brain regions. Many neurologi... more Myelin plays a crucial role in how well information travels between brain regions. Many neurological diseases affect the myelin in the white matter, making myelin-sensitive metrics derived from quantitative MRI of potential interest for early detection and prognosis of those conditions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin sensitive measure could result in a more complete model of structural brain connectivity and give better insight into how the myeloarchitecture relates to brain function. In this work we weight the connectome by the longitudinal relaxation rate (R1) as a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 is able to separate different classes (unimodal and transmodal), following a functional gradient. Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture.
ISMRM Annual Meeting
Despite recent advances in tractography, automatically reconstructing brain bundles in clinical s... more Despite recent advances in tractography, automatically reconstructing brain bundles in clinical settings remains a challenge. Here we present Q-FiberMapper (QFM), a framework that automatically preprocesses clinical data and reconstructs 33 major brain bundles with minimal user intervention. Furthermore, it derives insightful biomarkers and summarizes the findings in a human readable report. We validate QFM using a large cohort of 600 subjects, and show that it correctly reconstructs the shape and lateralization of major white-matter bundles. By simplifying and speeding the process of clinical reconstruction, QFM could help clinicians in the diagnosis and monitoring of brain pathologies.
A number of network structural characteristics have recently been the subject of particularly int... more A number of network structural characteristics have recently been the subject of particularly intense research, including degree distributions, community structure, and various measures of vertex centrality, to mention only a few. Vertices may have attributes associated with them; for example, properties of proteins in protein-protein interaction networks, users' social network profiles, or authors' publication histories in co-authorship networks. In a network, two vertices might be considered similar if they have similar attributes (features, properties), or they can be considered similar based solely on the network structure. Similarity of this type is called structural similarity, to distinguish it from properties similarity, social similarity, textual similarity, functional similarity or other similarity types found in networks. Here we focus on the similarity problem by computing (1) for each vertex a vector of structural features, called signature vector, based on the ...
Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography ... more Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and ...
Journal of Alzheimer's disease : JAD, 2020
BACKGROUND Vascular risk factors such as arterial stiffness play an important role in the etiolog... more BACKGROUND Vascular risk factors such as arterial stiffness play an important role in the etiology of Alzheimer's disease (AD), presumably due to the emergence of white matter lesions. However, the impact of arterial stiffness to white matter structure involved in the etiology of AD, including the corpus callosum remains poorly understood. OBJECTIVE The aims of the study are to better understand the relationship between arterial stiffness, white matter microstructure, and perfusion of the corpus callosum in older adults. METHODS Arterial stiffness was estimated using the gold standard measure of carotid-femoral pulse wave velocity (cfPWV). Cognitive performance was evaluated with the Trail Making Test part B-A. Neurite orientation dispersion and density imaging was used to obtain microstructural information such as neurite density and extracellular water diffusion. The cerebral blood flow was estimated using arterial spin labelling. RESULTS cfPWV better predicts the microstructu...
Network Neuroscience, 2021
Myelin plays a crucial role in how well information travels between brain regions. Complementing ... more Myelin plays a crucial role in how well information travels between brain regions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin-sensitive measure could result in a more complete model of structural brain connectivity and give better insight into white-matter myeloarchitecture. In this work we weight the connectome by the longitudinal relaxation rate (R1), a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 can be used to separate transmodal regions (responsible for higher-order functions) from unimodal regions (responsible for low-order functions). Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into accou...
Graphlet analysis is part of network theory that does not depend on the choice of the network nul... more Graphlet analysis is part of network theory that does not depend on the choice of the network null model and can provide comprehensive description of the local network structure. Here, we propose a signature vector for every vertex in the network and then the graphlet correlation matrix of the network. This analysis Moreover, by considering only those correlations (or anti correlations) in the correlation matrix that > <- The complexity of systems is frequently the result of non-trivial local connectivity and interaction of its constituents parts. A number of network structural characteristics have recently been the subject of particularly intense research, including degree distributions 1 , community structure 2,3 , and various measures of vertex centrality 4,5 , to mention only a few. Vertices may have attributes associated with them; for example, properties of proteins in protein-protein interaction networks 6 , users' social network profiles 7 , or authors' publication histories in co-authorship networks 8 . Two approaches that focus on the local connectivity of subgraphs within a network are Motifs and Graphlets. Motifs are defined as sub-graphs that repeat frequently in the networks i.e they repeat at frequency higher than in the random graphs 9,10 , and they depend on the choice of the network's null model. In contrast, graphlets are induced sub-graphs of a network that appear at any frequency and hence are independent of a null model. They have been introduced recently 11 and they have found numerous applications as building blocks of network analysis in various disciplines ranging from social science to biology . In social science, graphlet analysis (known as sub-graph census) is widely adopted in sociometric studies 12 . Much of the work in this vein focused on analyzing triadic tendencies as important structural features of social networks (e.g., transitivity or triadic closure) as well as analyzing triadic configurations as the basis for various social network theories (e.g., social balance, strength of weak ties, stability of ties, or trust ). In biology graphlets were used to infer protein structure 17 , to compare biological networks , and to characterize the relationship between disease and structure of networks 18 . Many of the real-world networks are directed, but until now no method has been proposed based on graphlets that can provide information about local structure of directed networks. Here, we offer a graphlet-based approach for analysis of the local structure of a directed network. In the method proposed in this manuscript, we compute for each vertex, a vector of structural features, called signature vector, based on the number of graphlets associated with the vertex, and for the network its graphlet correlation matrix, measuring graphlet dependencies which reveal unknown organizational principles of the network. We applied the technique to brain effective networks of 40 healthy subjects, and we found that many of the subjects share similar patterns in their network's local structure. In brain networks a node is associated with different types of elements, depending on the level of interest in the brain, and an edge represents the connection or interaction between two elements . If the brain is studied on
Hypertension
Hypertension, elevated morning blood pressure (BP) surges, and circadian BP variability constitut... more Hypertension, elevated morning blood pressure (BP) surges, and circadian BP variability constitute risk factors for cerebrovascular events. Nevertheless, while evidence indicates that hypertension is associated with cognitive dysfunctions, the link between BP variability and cognitive performance during aging is not clear. The purpose of this study is to determine the interaction between relative morning BP, cerebral blood flow (CBF) levels, and cognitive performance in hypertensive older adults with controlled BP under antihypertensive treatment. Eighty-four participants aged between 60 and 75 years old were separated into normotensive (n=51) and hypertensive (n=33) groups and underwent 24-hour ambulatory BP monitoring. They were also examined for CBF in the gray matter (CBF-GM) by magnetic resonance imaging and 5 cognitive domains: global cognition, working memory, episodic memory, processing speed, and executive functions. There was no difference in cognitive performance and CBF ...
Journal of Open Source Software
Magnetic resonance imaging (MRI) has revolutionized the way we look at the human body. However, c... more Magnetic resonance imaging (MRI) has revolutionized the way we look at the human body. However, conventional MR scanners are not measurement devices. They produce digital images represented by "shades of grey", and the intensity of the shades depends on the way the images are acquired. This is why it is difficult to compare images acquired at different clinical sites, limiting the diagnostic, prognostic, and scientific potential of the technology. Quantitative MRI (qMRI) aims to overcome this problem by assigning units to MR images, ensuring that the values represent a measurable quantity that can be reproduced within and across sites. While the vision for quantitative MRI is to overcome site-dependent variations, this is still a challenge due to variability in the hardware and software used by MR vendors to produce quantitative MRI maps.
Advances in Intelligent Systems and Computing, 2016
Graphlet analysis is part of network theory that does not depend on the choice of the network nul... more Graphlet analysis is part of network theory that does not depend on the choice of the network null model and can provide comprehensive description of the local network structure. Here, we propose a novel method for graphlet-based analysis of directed networks by computing first the signature vector for every vertex in the network and then the graphlet correlation matrix of the network. This analysis has been applied to brain effective connectivity networks by considering both direction and sign (inhibitory or excitatory) of the underlying directed (effective) connectivity. In particular, the signature vectors for brain regions and the graphlet correlation matrices of the brain effective network are computed for 40 healthy subjects and common dependencies are revealed. We found that the signature vectors (node, wedge, and triangle degrees) are dominant for the excitatory effective brain networks. Moreover, by considering only those correlations (or anti correlations) in the correlation matrix that are significant (>0.7 or <−0.7) and are presented in more than 60% of the subjects, we found that excitatory effective brain networks show stronger causal (measured with Granger causality) patterns (G-causes and G-effects) than inhibitory effective brain networks. The complexity of systems is frequently the result of non-trivial local connectivity and interaction of its constituents parts. A number of network structural characteristics have recently been the subject of particularly intense research, including degree distributions 1 , community structure 2,3 , and various measures of vertex cen-trality 4,5 , to mention only a few. Vertices may have attributes associated with them; for example, properties of proteins in protein-protein interaction networks 6 , users' social network profiles 7 , or authors' publication histories in co-authorship networks 8. Two approaches that focus on the local connectivity of subgraphs within a network are Motifs and Graphlets. Motifs are defined as sub-graphs that repeat frequently in the networks i.e they repeat at frequency higher than in the random graphs 9,10 , and they depend on the choice of the network's null model. In contrast, graphlets are induced sub-graphs of a network that appear at any frequency and hence are independent of a null model. They have been introduced recently 11 and they have found numerous applications as building blocks of network analysis in various disciplines ranging from social science 12,13 to biology 14,15. In social science, graphlet analysis (known as sub-graph census) is widely adopted in sociometric studies 12. Much of the work in this vein focused on analyzing triadic tendencies as important structural features of social networks (e.g., transi-tivity or triadic closure) as well as analyzing triadic configurations as the basis for various social network theories (e.g., social balance, strength of weak ties, stability of ties, or trust 16). In biology graphlets were used to infer protein structure 17 , to compare biological networks 14,15 , and to characterize the relationship between disease and structure of networks 18. Many of the real-world networks are directed, but until now no method has been proposed based on graphlets that can provide information about local structure of directed networks. Here, we offer a graphlet-based approach for analysis of the local structure of a directed network. In the method proposed in this manuscript, we compute for each vertex, a vector of structural features, called signature vector, based on the number of graphlets associated with the vertex, and for the network its graphlet correlation matrix, measuring graphlet dependencies which reveal unknown organizational principles of the network. We applied the technique to brain effective networks of 40 healthy subjects, and we found that many of the subjects share similar patterns in their network's local structure. In brain networks a node is associated with different types of elements, depending on the level of interest in the brain, and an edge represents the connection or interaction between two elements 19. If the brain is studied on
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Papers by Tommy Boshkovski