Applying Diffusion MRI in Population Studies
Ragini Verma

Synopsis

The talk will cover various aspects of processing and analysis of diffusion data, from the perspective of population studies. Specifically it will discuss connectome creation, connectomic analysis, automated tract extraction and biomarker creation. In addition to describing the protocol for cross-sectional studies, we will also discuss the extension of these methods to longitudinal studies. In addition to the description of methods, application to clinical populations will be presented.

The talk will cover various aspects of processing and analysis of diffusion data, from the perspective of hypotheses associated with population studies. Specifically it will discuss (1) connectome creation - atlas and structure-based parcellation, tracking and choice of connectivity measures; (2) connectomic analysis – edge-based statistics, extraction of meso-scale structures (subnetworks and core-periphery), and their application to discriminative and regressive hypotheses; (3) tract-based analysis - automated tract extraction that does not require the placement of ROIs; and (4) biomarker creation. In addition to describing the protocol for cross-sectional studies, we will also discuss the extension of these methods to longitudinal studies.

Creation of Connectome

A connectome is mathematically described as a graph G=(V,E), consisting of nodes V representing brain regions, and a set of edges E, representing connection between these regions, weighted with a connectivity measure [1, 2]. A matrix representation of this graph is used for all methodological derivations. Connectomes (Fig. 1) are created by parcellating the brain into regions, tracking between regions, and deriving a measure based on the tracking that will quantify connectivity strength. As there is no consensus in literature on methods to build the connectome, as a pre-processing step, we will discuss several types of connectomes by varying parcellation, tractography and connectivity measures. Namely (A) Parcellation: Atlas-based and structural-connectivity based; (B) Tracking algorithm: probabilistic tractography (probtrackx from FSL [3]) and deterministic tracking (Trackvis [4, 5]), comparison based on the density of the connectome [3, 6]. (C) Connectivity Measure is the number assigned to each edge representing connectivity strength. We will use (1) Binary where weights of edges are set to 1 or 0, based on whether there is at least one streamline connecting the regions; (2) Streamline count: number of streamlines between regions; (3) Incorporating ROI volume by normalizing the streamline count with the sum of region volumes. This accounts for the fact that due to the non-uniform sizes of parcellated regions, bigger regions may have a higher probability of being touched by a streamline; and (4) Incorporating fiber length: due to the possible distal bias in tractography, that is, the number of fibers between two regions decreases with distance [7, 8], as a larger number of propagation steps need to be traversed, streamline count will be normalized by the average inter-regional fiber length. These measures can be used to create an n x n (n is the number of regions used in parcellation) matrix representation of the connectome. The pros and cons of the measures will be discussed.

Connectomic analysis

We will begin with a description of edge-based analysis [9]. Connectomes are created by parcellating the WM/GM boundary of the brain into “regions” and tracking between them using a fiber tracking algorithm, with connectivity measures created by averaging connectivity in each region, producing a network representation of the brain. Thus the study of networks predicates on the ability of methods in succinctly extracting meaningful representative connectivity information from these connectomes at the subject and population level. We will briefly review the considerable amount of work that has been done in extracting local and global network features [2, 10-12]. We will the focus on describing meso-scale structures, which are groupings of nodes and their communication patterns that characterize the dominant organizational structure (communities/ subnetwork) of the brain, as well as its auxiliary characteristics (core-periphery). Identification of meso-scale structures can reveal how the network manages an interplay of the seemingly competing principles of functional segregation and integration, which in turn lead to a complex behavioral repertoire. We will describe methods that can extract underlying network patterns (meso-scale structures) that characterize the connectivity variation in a population with separate components components pertaining to each source of variation, while also capturing variations at the subject-specific level [13-17]. The decomposition maintains the interpretation of each component as a subnetwork and the coefficients associated with these components as the weight of the subnetwork, while providing a succinct low dimensional representation of the population amenable to statistical analysis. In addition to determining the connectivity patterns of variability, the projection of the subject networks into the basis set, provides a low dimensional representation of it that teases apart different sources of variation in the sample, facilitating variation-specific statistical analysis, as well as the ability to monitor subject-specific changes in longitudinal studies. We will also discuss a unified formulation for the algorithmic detection and analysis of hybrid meso-scale structures that captures the interplay between multiple meso-scale structures and statistical comparison of competing structures [13]. These methods have been applied to ASD, TBI and developmental populations [14, 15, 17-20].

Tract-based analysis

Advances in diffusion imaging and tractography techniques have led to an increasing interest in tract-based analysis. Statistical analyses over white matter tracts can contribute greatly towards understanding structural mechanisms of the brain since tracts are representative of the connectivity pathways. Automated extraction of eloquent tracts is of crucial importance in neurosurgical planning, which requires the knowledge of placement of tracts with regards to the tumor. The main challenge with tract-based studies is the extraction of tracts in a consistent and comparable manner over a large group of individuals without drawing the inclusion and exclusion regions of interest, or using shape based tract features that may alter in the presence of pathology like tumors. We will describe a framework for automated extraction of WM tracts, in which connectivity signatures are created for each fiber [21] using probabilistic tracking [3] from each voxel to parcelleted regions of subject’s brain [22]. A group-wise clustering of these fibers on healthy controls is used to generate a fiber bundle atlas. Finally, Adaptive Clustering incorporates the previously generated clustering atlas as a prior, to cluster the fibers of a new subject automatically. By alleviating the seed selection or inclusion/exclusion ROI drawing requirements that are usually handled by trained radiologists, this framework expands the range of possible clinical applications, especially surgical planning [23], and establishes the ability to perform tract-based analysis with large samples, which will be discussed in the talk.

Creation of biomarker

Connectomes and tracts are imaging features that provide non-invasive insight into brain mechanisms. However, univariate statistical analysis in which each feature (e.g. mean subnetwork connectivity) was treated individually, without accounting for multivariate interactions between features, do not accurately represent complex (non-linear) developmental patterns in a developing brain [24]. Linear and non-linear classification approaches like kernel support vector machine (SVM) [25, 26] and deep learning (DL) [27-29] are now routinely used to create population markers by learning complex multivariate relationships between features and finding high-dimensional brain patterns representative of disorder-induced alterations. These assign a quantitative subject-wise score that can be correlated with age or diagnostic/behavioral measures and can help in assessing treatment and disease progression. Analogously, Support Vector Regression (SVR) can learn brain patterns to predict continuous variables like age and diagnostic/behavioral measures. Imaging-based markers of pathology [30-38] (using structural imaging [30-33], and more recently DTI [35-38]), and brain age [39] have been created. We will discuss non-linear machine learning approaches for creating network-based biomarkers.

Acknowledgements

No acknowledgement found.

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