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

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From: Neural Regeneration Research(Vol. 16, Issue 5)
Publisher: Medknow Publications and Media Pvt. Ltd.
Document Type: Report
Length: 7,838 words
Lexile Measure: 1530L

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Byline: Erin. Kaiser, J. Poythress, Kelly. Scheulin, Brian. Jurgielewicz, Nicole. Lazar, Cheolwoo. Park, Steven. Stice, Jeongyoun. Ahn, Franklin. West

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.

Introduction

Projections show that within the next 10 years an additional 3.4 million Americans over the age of 18 will suffer a stroke (Ovbiagele et al., 2013). With limited Food and Drug Administration (FDA)-approved therapies, stroke has quickly become one of the leading causes of long-term disability. Statistics show that > 50% of stroke survivors experience a reduction in mobility of which 25-50% require some degree of assistance and ~50% experience long-term dependency (Benjamin et al., 2019; Miller et al., 2010). Early predictions of long-term functional recovery are therefore critical for determining patient prognosis and realistic rehabilitation goals as well as informing patients and caregivers of potential home adjustments and necessary community support (Kwakkel et al., 2011). Expected functional recovery projections could also be utilized to group patient populations enrolled in clinical trials in order to improve the accuracy in which novel therapeutic interventions are assessed (Young et al., 2005; Menaa, 2013). Neuroimaging modalities including magnetic resonance imaging (MRI) are widely used for diagnosing acute ischemic stroke and could provide important insight into identifying key stroke pathologies that influence optimal post-stroke motor recovery (Campbell et al., 2012).

Multiplanar MRI has significant potential to reliably predict post-stroke recovery as it can provide high-resolution structural information post-stroke. In particular, diffusion tensor imaging (DTI)-derived fractional anisotropy (FA) values have been used to estimate motor function in patients (Puig et al., 2011; Schulz et al., 2012; Groisser et al., 2014). Although differing prediction potentials have been reported between specific stroke phases (e.g., acute, chronic), FA reductions in the subacute phase (24...

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Gale Document Number: GALE|A642710618