Brain network efficiency is influenced by the pathologic source of corticobasal syndrome

Abstract

Objective:To apply network-based statistics to diffusion-weighted imaging tractography data and detect Alzheimer disease vs non-Alzheimer degeneration in the context of corticobasal syndrome.Methods:In a cross-sectional design, pathology was confirmed by autopsy or a pathologically validated CSF total tau-to-β-amyloid ratio (T-tau/Aβ). Using structural MRI data, we identify association areas in fronto-temporo-parietal cortex with reduced gray matter density in corticobasal syndrome (n = 40) relative to age-matched controls (n = 40). Using these fronto-temporo-parietal regions of interest, we construct structural brain networks in clinically similar subgroups of individuals with Alzheimer disease (n = 21) or non-Alzheimer pathology (n = 19) by linking these regions by the number of white matter streamlines identified in a deterministic tractography analysis of diffusion tensor imaging data. We characterize these structural networks using 5 graph-based statistics, and assess their relative utility in classifying underlying pathology with leave-one-out cross-validation using a supervised support vector machine.Results:Gray matter density poorly discriminates between Alzheimer disease and non-Alzheimer pathology subgroups with low sensitivity (57%) and specificity (52%). In contrast, a statistic of local network efficiency demonstrates very good discriminatory power, with 85% sensitivity and 84% specificity.Conclusions:Our results indicate that the underlying pathologic sources of corticobasal syndrome can be classified more accurately using graph theoretical statistics derived from patterns of white matter network organization in association cortex than by regional gray matter density alone. These results highlight the importance of a multimodal neuroimaging approach to diagnostic analyses of corticobasal syndrome.

A Graph Signal Processing Perspective on Functional Brain Imaging

“…Enables the ability to process visual detail has been reliably associated with neurological [61], [62] and psychological [63] disorders. Besides structural connectivity, it is also possible to acquire brain activity signals x ∈ R N such that the value of the i th component x i quantifies neuronal activity in brain region isee Figure 2 for an illustration of these BOLD signals and Callout 2 for details on the methods.…”

Hierarchical Clustering Given Confidence Intervals of Metric Distances

“…as the second inequality in (31). From Theorem 3, cluster-and-combine clustering u CL X applied to I X = (X, d X ,d X ) yields a minimal ultrametric among outputs by all methods satisfying axioms (A1)-(A2).…”

“…Therefore, we can claim that u CL X (x, x ) > δ implies u X (x, x ) > δ. Because this statement is true for any δ > 0, it induces that u CL X (x, x ) ≤ u X (x, x ) for any x = x ∈ X as the first inequality in (31).…”

“…To show the second inequality in (31), first notice that for any distinct nodes x i = x j ∈ X, we can construct a two-node space ∆ i,j = {p, q}, d X (x i , x j ),d X (x i , x j ) and a mapping φ i,j : {p, q} → X with φ i,j (p) = x i and φ i,j (q) = x j such that φ i,j is α-distancereducing. To demonstrate this, we need to verify conditions (13) and (14).…”

Enhancing Geometric Deep Learning via Graph Filter Deconvolution

“…Graph signal processing (GSP) emerges in response to the need to better process and understand the ever-increasing volume of network data, often conceptualized as signals defined on graphs [1], [2]. For example, graph-supported signals can model economic activity observed over a network of production flows between industrial sectors [3], as well as brain activity signals supported on brain connectivity networks [4]- [6]. However, due to the complexity and irregularity of such networks, most of the standard signal processing notions -such as convolution and downsampling -are no longer directly applicable on graph settings.…”