An integrative multivariate approach for predicting functional recovery using magnetic resonance imaging parameters in a translational pig ischemic stroke model

Abstract

Magnetic resonance imaging (MRI) is a clinically relevant, real-time imaging modality that is frequently utilized to assess stroke type and severity. However, specific MRI biomarkers that can be used to predict long-term functional recovery are still a critical need. Consequently, the present study sought to examine the prognostic value of commonly utilized MRI parameters to predict functional outcomes in a porcine model of ischemic stroke. Stroke was induced via permanent middle cerebral artery occlusion. At 24 hours post-stroke, MRI analysis revealed focal ischemic lesions, decreased diffusivity, hemispheric swelling, and white matter degradation. Functional deficits including behavioral abnormalities in open field and novel object exploration as well as spatiotemporal gait impairments were observed at 4 weeks post-stroke. Gaussian graphical models identified specific MRI outputs and functional recovery variables, including white matter integrity and gait performance, that exhibited strong conditional dependencies. Canonical correlation analysis revealed a prognostic relationship between lesion volume and white matter integrity and novel object exploration and gait performance. Consequently, these analyses may also have the potential of predicting patient recovery at chronic time points as pigs and humans share many anatomical similarities (e.g., white matter composition) that have proven to be critical in ischemic stroke pathophysiology. The study was approved by the University of Georgia (UGA) Institutional Animal Care and Use Committee (IACUC; Protocol Number: A2014-07-021-Y3-A11 and 2018-01-029-Y1-A5) on November 22, 2017.

Keywords: Gaussian graphical models; behavior testing; canonical correlation analysis; gait analysis; ischemic stroke; magnetic resonance imaging; pig model; principal component analysis.

Figure 1

Magnetic resonance imaging confirms permanent middle cerebral artery occlusion results in ischemic stroke. Pathological changes including hyperintense lesion formation was observed in T2 weighted (T2W; A), T2 fluid attenuated inversion recovery (T2F; B), and diffision weighted imaging (DWI; C) sequences (white arrows). Corresponding hypointensities indicative of decreased diffusivity and cytotoxic edema were observed on apparent diffusion coefficient (ADC; D) maps (white arrow). Loss of white matter integrity was observed in the internal capsule (IC; white arrow) and corpus callosum (CC) utilizing fractional anisotropy (FA) maps (E).

Figure 2

Ischemic stroke results in functional impairments as assessed by behavior testing and spatiotemporal gait analysis. Ethovision XT tracking software was utilized during open field (OF; A and B) and novel object recognition (NOR; C and D) testing to automatically assess differences in distance traveled, velocity, moving percent, and exploration of novel (yellow circles) and familiar objects (pink circles); representative movement tracings (blue lines) are shown pre- (A and C) and post-stroke (B and D). Additional motor function data was automatically analyzed using GAITFour® software to provide quantitative measurements of 36 gait variables; representative gait tracings shown for the left front (blue circles), right front (red circles), left hind (green circles), right hind (black circles) hooves (E).

Figure 3

Heat map of Pearson correlations between gait variables. Gait parameters exhibited a high degree of overlapping information, as indicated by the presence of many large magnitude Pearson correlations. Overlapping information is caused by small variability across the four paws (e.g., cycle time) or redundancy between parameters (e.g., stance time with cycle time). %TPI: Total pressure index; TSP: total scaled pressure; LF: left front; LH: left hind; RF: right front; RH: right hind.

Figure 4

Estimated precision matrix visualized as a network for a sequence of values of the graphical lasso regularization parameter λ. Connections among magnetic resonance imaging variables 24 hours post-stroke (green lines). Connections among behavior and gait variables 4 weeks post-stroke (blue lines). Connections between MRI variables and behavior and gait variables across time points (pink lines). λ = 0.600 (A), 0.616 (B), 0.640 (C), 0.820 (D). See Table 1 for definitions of assessed magnetic resonance and functional outcome variables.