Integrative Structural Brain Network Analysis in Diffusion Tensor Imaging

Topological Embedding of Human Brain Networks with Applications to Dynamics of Temporal Lobe Epilepsy

We introduce a novel, data-driven topological data analysis (TDA) approach for embedding brain networks into a lower-dimensional space in quantifying the dynamics of temporal lobe epilepsy (TLE) obtained from resting-state functional magnetic resonance imaging (rs-fMRI). This embedding facilitates the orthogonal projection of 0D and 1D topological features, allowing for the visualization and modeling of the dynamics of functional human brain networks in a resting state. We then quantify the topological disparities between networks to determine the coordinates for embedding. This framework enables us to conduct a coherent statistical inference within the embedded space. Our results indicate that brain network topology in TLE patients exhibits increased rigidity in 0D topology but more rapid flections compared to that of normal controls in 1D topology.

Topological state-space estimation of functional human brain networks

We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. Our method utilizes the Wasserstein distance to measure topological differences, enabling the clustering of brain networks into distinct topological states. This technique outperforms the commonly used k-means clustering in identifying brain network state spaces by effectively incorporating the temporal dynamics of the data without the need for explicit model specification. We further investigate the genetic underpinnings of these topological features using a twin study design, examining the heritability of such state changes. Our findings suggest that the topology of brain networks, particularly in their dynamic state changes, may hold significant hidden genetic information.

Altered topological structure of the brain white matter in maltreated children through topological data analysis

Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging and diffusion tensor imaging. We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children with a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.

Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance

Persistent homology offers a powerful tool for extracting hidden topological signals from brain networks. It captures the evolution of topological structures across multiple scales, known as filtrations, thereby revealing topological features that persist over these scales. These features are summarized in persistence diagrams, and their dissimilarity is quantified using the Wasserstein distance. However, the Wasserstein distance does not follow a known distribution, posing challenges for the application of existing parametric statistical models. To tackle this issue, we introduce a unified topological inference framework centered on the Wasserstein distance. Our approach has no explicit model and distributional assumptions. The inference is performed in a completely data driven fashion. We apply this method to resting-state functional magnetic resonance images (rs-fMRI) of temporal lobe epilepsy patients collected from two different sites: the University of Wisconsin-Madison and the Medical College of Wisconsin. Importantly, our topological method is robust to variations due to sex and image acquisition, obviating the need to account for these variables as nuisance covariates. We successfully localize the brain regions that contribute the most to topological differences. A MATLAB package used for all analyses in this study is available at https://github.com/laplcebeltrami/PH-STAT.

Altered Topological Structure of the Brain White Matter in Maltreated Children through Topological Data Analysis

Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white-matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children to a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.

Exploratory Correlation of The Human Structural Connectome with Non-MRI Variables in Alzheimer's Disease

INTRODUCTION

Discovery of the associations between brain structural connectivity and clinical and demographic variables can help to better understand the vulnerability and resilience of the brain architecture to neurodegenerative diseases and to discover biomarkers.

METHODS

We used four diffusion-MRI databases, three related to Alzheimer's disease, to exploratorily correlate structural connections between 85 brain regions with non-MRI variables, while stringently correcting the significance values for multiple testing and ruling out spurious correlations via careful visual inspection. We repeated the analysis with brain connectivity augmented with multi-synaptic neural pathways.

RESULTS

We found 85 and 101 significant relationships with direct and augmented connectivity, respectively, which were generally stronger for the latter. Age was consistently linked to decreased connectivity, and healthier clinical scores were generally linked to increased connectivity.

DISCUSSION

Our findings help to elucidate which structural brain networks are affected in Alzheimer's disease and aging and highlight the importance of including indirect connections.

Exploratory correlation of the human structural connectome with non-MRI variables in Alzheimer's disease

Introduction

Discovery of the associations between brain structural connectivity and clinical and demographic variables can help to better understand the vulnerability and resilience of the brain architecture to neurodegenerative diseases and to discover biomarkers.

Methods

We used four diffusion-MRI databases, three related to Alzheimer's disease (AD), to exploratorily correlate structural connections between 85 brain regions with non-MRI variables, while stringently correcting the significance values for multiple testing and ruling out spurious correlations via careful visual inspection. We repeated the analysis with brain connectivity augmented with multi-synaptic neural pathways.

Results

We found 85 and 101 significant relationships with direct and augmented connectivity, respectively, which were generally stronger for the latter. Age was consistently linked to decreased connectivity, and healthier clinical scores were generally linked to increased connectivity.

Discussion

Our findings help to elucidate which structural brain networks are affected in AD and aging and highlight the importance of including indirect connections.

Unified Topological Inference for Brain Networks in Temporal Lobe Epilepsy Using the Wasserstein Distance

Persistent homology offers a powerful tool for extracting hidden topological signals from brain networks. It captures the evolution of topological structures across multiple scales, known as filtrations, thereby revealing topological features that persist over these scales. These features are summarized in persistence diagrams, and their dissimilarity is quantified using the Wasserstein distance. However, the Wasserstein distance does not follow a known distribution, posing challenges for the application of existing parametric statistical models. To tackle this issue, we introduce a unified topological inference framework centered on the Wasserstein distance. Our approach has no explicit model and distributional assumptions. The inference is performed in a completely data driven fashion. We apply this method to resting-state functional magnetic resonance images (rs-fMRI) of temporal lobe epilepsy patients collected from two different sites: the University of Wisconsin-Madison and the Medical College of Wisconsin. Importantly, our topological method is robust to variations due to sex and image acquisition, obviating the need to account for these variables as nuisance covariates. We successfully localize the brain regions that contribute the most to topological differences. A MATLAB package used for all analyses in this study is available at https://github.com/laplcebeltrami/PH-STAT.

Microstructural Properties of Brain White Matter Tracts in Breast Cancer Survivors: A Diffusion Tensor Imaging Study

Complex breast cancer (BC) treatment can cause various neurological and psychiatric complications, such as postmastectomy pain syndrome, vestibulocerebellar ataxia, and depression, which can lead to microstructural damage of the white matter tracts of the brain. The purpose of the study is to assess microstructural changes in the white matter tracts of the brain in BC survivors using diffusion tensor imaging (DTI). Single DTI scans were performed on patients (n = 84) after complex BC treatment (i.e., surgery, chemotherapy and/or radiation therapy) and on the control group (n = 40). According to the results, a decrease in the quantitative anisotropy (FDR ≤ 0.05) was revealed in the bilateral corticospinal tracts, cerebellar tracts, corpus callosum, fornix, left superior corticostriatal and left corticopontine parietal in patients after BC treatment in comparison to the control group. A decrease in the quantitative anisotropy (FDR ≤ 0.05) was also revealed in the corpus callosum and right cerebellar tracts in patients after BC treatment with the presence of postmastectomy pain syndrome and vestibulocerebellar ataxia. The use of DTI in patients after BC treatment reveals microstructural properties of the white matter tracts in the brain. The results will allow for the improvement of treatment and rehabilitation approaches in patients receiving treatment for breast cancer.

Dyconnmap: Dynamic connectome mapping-A neuroimaging python module

Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via a more extensive repertoire of functional associations. Network neuroscience is a relatively new multidisciplinary scientific avenue of the study of complex systems by pursuing novel ways to analyze, map, store and model the essential elements and their interactions in complex neurobiological systems, particularly the human brain, the most complex system in nature. Due to a rapid expansion of neuroimaging data sets' size and complexity, it is essential to propose and adopt new empirical tools to track dynamic patterns between neurons and brain areas and create comprehensive maps. In recent years, there is a rapid growth of scientific interest in moving functional neuroimaging analysis beyond simplified group or time‐averaged approaches and sophisticated algorithms that can capture the time‐varying properties of functional connectivity. We describe algorithms and network metrics that can capture the dynamic evolution of functional connectivity under this perspective. We adopt the word ‘chronnectome’ (integration of the Greek word ‘Chronos’, which means time, and connectome) to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way. We also describe how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which our brain evolved. This characteristic supports our complex human mind. The temporal evolution of these brain states and well‐known network metrics could give rise to new analytic trends. Functional brain networks could also increase the multi‐faced nature of the dynamic networks revealing complementary information. Finally, we describe a python module (https://github.com/makism/dyconnmap) which accompanies this article and contains a collection of dynamic complex network analytics and measures and demonstrates its great promise for the study of a healthy subject's repeated fMRI scans.

Müller glia-myeloid cell crosstalk accelerates optic nerve regeneration in the adult zebrafish

Neurodegenerative disorders, characterized by progressive neuronal loss, eventually lead to functional impairment in the adult mammalian central nervous system (CNS). Importantly, these deteriorations are irreversible, due to the very limited regenerative potential of these CNS neurons. Stimulating and redirecting neuroinflammation was recently put forward as an important approach to induce axonal regeneration, but it remains elusive how inflammatory processes and CNS repair are intertwined. To gain more insight into these interactions, we investigated how immunomodulation affects the regenerative outcome after optic nerve crush (ONC) in the spontaneously regenerating zebrafish. First, inducing intraocular inflammation using zymosan resulted in an acute inflammatory response, characterized by an increased infiltration and proliferation of innate blood-borne immune cells, reactivation of Müller glia, and altered retinal cytokine expression. Strikingly, inflammatory stimulation also accelerated axonal regrowth after optic nerve injury. Second, we demonstrated that acute depletion of both microglia and macrophages in the retina, using pharmacological treatments with both the CSF1R inhibitor PLX3397 and clodronate liposomes, compromised optic nerve regeneration. Moreover, we observed that csf1ra/b double mutant fish, lacking microglia in both retina and brain, displayed accelerated RGC axonal regrowth after ONC, which was accompanied with unusual Müller glia proliferative gliosis. Altogether, our results highlight the importance of altered glial cell interactions in the axonal regeneration process after ONC in adult zebrafish. Unraveling the relative contribution of the different cell types, as well as the signaling pathways involved, may pinpoint new targets to stimulate repair in the vertebrate CNS.

Altered brain white matter connectome in children and adolescents with prenatal alcohol exposure

Diffuson tensor imaging (DTI) has demonstrated widespread alterations of brain white matter structure in children with prenatal alcohol exposure (PAE), yet it remains unclear how these alterations affect the structural brain network as a whole. The present study aimed to examine changes in the DTI-based structural connectome in children and adolescents with PAE compared to unexposed controls. Participants were 121 children and adolescents with PAE (51 females) and 119 typically-developing controls (49 females) aged 5-18 years with DTI data collected at one of four research centers across Canada. Graph-theory based analysis was performed on the connectivity matrix constructed from whole-brain white matter fibers via deterministic tractography. The PAE group had significantly decreased whole-brain global efficiency, degree centrality, and participation coefficients, as well as increased shortest path length and betweenness centrality compared to unexposed controls. Individuals with PAE had decreased connectivity between the attention, somatomotor, and default mode networks compared to controls. This study demonstrates decreased structural white matter connectivity in children and adolescents with PAE at a whole-brain level, suggesting widespread alterations in how networks are connected with each other. This decreased connectivity may underlie cognitive and behavioural difficulties in children with PAE.

Characterization complex collagen fiber architecture in knee joint using high-resolution diffusion imaging

PURPOSE

To evaluate the complex fiber orientations and 3D collagen fiber network of knee joint connective tissues, including ligaments, muscle, articular cartilage, and meniscus using high spatial and angular resolution diffusion imaging.

METHODS

Two rat knee joints were scanned using a modified 3D diffusion-weighted spin echo pulse sequence with the isotropic spatial resolution of 45 μm at 9.4T. The b values varied from 250 to 1250 s/mm² with 31 diffusion encoding directions for 1 rat knee. The b value was fixed to 1000 s/mm² with 147 diffusion encoding directions for the second knee. Both the diffusion tensor imaging (DTI) model and generalized Q-sampling imaging (GQI) method were used to investigate the fiber orientation distributions and tractography with the validation of polarized light microscopy.

RESULTS

To better resolve the crossing fibers, the b value should be great than or equal to 1000 s/mm² . The tractography results were comparable between the DTI model and GQI method in ligament and muscle. However, the tractography exhibited apparent difference between DTI and GQI in connective tissues with more complex collagen fibers network, such as cartilage and meniscus. In articular cartilage, there were numerous crossing fibers found in superficial zone and transitional zone. Tractography generated with GQI also resulted in more intact tracts in articular cartilage than DTI.

CONCLUSION

High-resolution diffusion imaging with GQI method can trace the complex collagen fiber orientations and architectures of the knee joint at microscopic resolution.

Heritability of nested hierarchical structural brain network

When a brain network is constructed by an existing parcellation method, the topological structure of the network changes depending on the scale of the parcellation. To avoid the scale dependency, we propose to construct a nested hierarchical structural brain network by subdividing the existing parcellation hierarchically. The method is applied in diffusion tensor imaging study of 111 twins in characterizing the topology of the brain network. The genetic contribution of the whole brain structural connectivity is determined and shown to be robustly present over different network scales.

Quantification of structural brain connectivity via a conductance model

Connectomics has proved promising in quantifying and understanding the effects of development, aging and an array of diseases on the brain. In this work, we propose a new structural connectivity measure from diffusion MRI that allows us to incorporate direct brain connections, as well as indirect ones that would not be otherwise accounted for by standard techniques and that may be key for the better understanding of function from structure. From our experiments on the Human Connectome Project dataset, we find that our measure of structural connectivity better correlates with functional connectivity than streamline tractography does, meaning that it provides new structural information related to function. Through additional experiments on the ADNI-2 dataset, we demonstrate the ability of this new measure to better discriminate different stages of Alzheimer's disease. Our findings suggest that this measure is useful in the study of the normal brain structure, and for quantifying the effects of disease on the brain structure.

Statistical Challenges of Big Brain Network Data

We explore the main characteristics of big brain network data that offer unique statistical challenges. The brain networks are biologically expected to be both sparse and hierarchical. Such unique characterizations put specific topological constraints onto statistical approaches and models we can use effectively. We explore the limitations of the current models used in the field and offer alternative approaches and explain new challenges.