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BackgroundHealthy articular cartilage presents structural gradients defined by distinct zonal patterns through the thickness, which may be disrupted in the pathogenesis of several disorders. Analysis of textural patterns using quantitative MRI data may identify structural gradients of healthy or degenerating tissue that correlate with early osteoarthritis (OA). PurposeTo quantify spatial gradients and patterns in MRI data, and to probe new candidate biomarkers for early severity of OA. Study TypeRetrospective study. SubjectsFourteen volunteers receiving total knee replacement surgery (eight males/two females/four unknown, average age ± standard deviation: 68.1 ± 9.6 years) and 10 patients from the OA Initiative (OAI) with radiographic OA onset (two males/eight females, average age ± standard deviation: 57.7 ± 9.4 years; initial Kellgren‐Lawrence [KL] grade: 0; final KL grade: 3 over the 10‐year study). Field Strength/Sequence3.0‐T and 14.1‐T, biomechanics‐based displacement‐encoded imaging, fast spin echo, multi‐slice multi‐echoT2mapping. AssessmentWe studied structure and strain in cartilage explants from volunteers receiving total knee replacement, or structure in cartilage of OAI patients with progressive OA. We calculated spatial gradients of quantitative MRI measures (eg, T2) normal to the cartilage surface to enhance zonal variations. We compared gradient values against histologically OA severity, conventional relaxometry, and/or KL grades. Statistical TestsMultiparametric linear regression for evaluation of the relationship between residuals of the mixed effects models and histologically determined OA severity scoring, with a significance threshold atα = 0.05. ResultsGradients of individual relaxometry and biomechanics measures significantly correlated with OA severity, outperforming conventional relaxometry and strain metrics. In human explants, analysis of spatial gradients provided the strongest relationship to OA severity (R2 = 0.627). Spatial gradients of T2 from OAI data identified variations in radiographic (KL Grade 2) OA severity in single subjects, while conventional T2 alone did not. Data ConclusionSpatial gradients of quantitative MRI data may improve the predictive power of noninvasive imaging for early‐stage degeneration. Evidence Level1 Technical EfficacyStage 1
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Independent Component and Graph Theory Analyses Reveal Normalized Brain Networks on Resting‐State Functional MRI After Working Memory Training in People With HIV
BackgroundCognitive training may partially reverse cognitive deficits in people with HIV (PWH). Previous functional MRI (fMRI) studies demonstrate that working memory training (WMT) alters brain activity during working memory tasks, but its effects on resting brain network organization remain unknown. PurposeTo test whether WMT affects PWH brain functional connectivity in resting‐state fMRI (rsfMRI). Study TypeProspective. PopulationA total of 53 PWH (ages 50.7 ± 1.5 years, two women) and 53HIV‐seronegative controls (SN, ages 49.5 ± 1.6 years, six women). Field Strength/SequenceAxial single‐shot gradient‐echo echo‐planar imaging at 3.0 T was performed at baseline (TL1), at 1‐month (TL2), and at 6‐months (TL3), after WMT. AssessmentAll participants had rsfMRI and clinical assessments (including neuropsychological tests) at TL1 before randomization to Cogmed WMT (adaptive training,n = 58: 28 PWH, 30 SN; nonadaptive training,n = 48: 25 PWH, 23 SN), 25 sessions over 5–8 weeks. All assessments were repeated at TL2 and at TL3. The functional connectivity estimated by independent component analysis (ICA) or graph theory (GT) metrics (eigenvector centrality, etc.) for different link densities (LDs) were compared between PWH and SN groups at TL1 and TL2. Statistical TestsTwo‐way analyses of variance (ANOVA) on GT metrics and two‐samplet‐tests on FC or GT metrics were performed. Cognitive (eg memory) measures were correlated with eigenvector centrality (eCent) using Pearson's correlations. The significance level was set atP < 0.05 after false discovery rate correction. ResultsThe ventral default mode network (vDMN) eCent differed between PWH and SN groups at TL1 but not at TL2 (P = 0.28). In PWH, vDMN eCent changes significantly correlated with changes in the memory ability in PWH (r = −0.62 at LD = 50%) and vDMN eCent before training significantly correlated with memory performance changes (r = 0.53 at LD = 50%). Data ConclusionICA and GT analyses showed that adaptive WMT normalized graph properties of the vDMN in PWH. Evidence Level1 Technical Efficacy1
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- Award ID(s):
- 1631838
- PAR ID:
- 10405630
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Journal of Magnetic Resonance Imaging
- Volume:
- 57
- Issue:
- 5
- ISSN:
- 1053-1807
- Page Range / eLocation ID:
- p. 1552-1564
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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