Dataset Information

Uncovering associations between data-driven learned qMRI biomarkers and chronic pain.

ABSTRACT: Knee pain is the most common and debilitating symptom of knee osteoarthritis (OA). While there is a perceived association between OA imaging biomarkers and pain, there are weak or conflicting findings for this relationship. This study uses Deep Learning (DL) models to elucidate associations between bone shape, cartilage thickness and T₂ relaxation times extracted from Magnetic Resonance Images (MRI) and chronic knee pain. Class Activation Maps (Grad-CAM) applied on the trained chronic pain DL models are used to evaluate the locations of features associated with presence and absence of pain. For the cartilage thickness biomarker, the presence of features sensitive for pain presence were generally located in the medial side, while the features specific for pain absence were generally located in the anterior lateral side. This suggests that the association of cartilage thickness and pain varies, requiring a more personalized averaging strategy. We propose a novel DL-guided definition for cartilage thickness spatial averaging based on Grad-CAM weights. We showed a significant improvement modeling chronic knee pain with the inclusion of the novel biomarker definition: likelihood ratio test p-values of 7.01 × 10^-33 and 1.93 × 10^-14 for DL-guided cartilage thickness averaging for the femur and tibia, respectively, compared to the cartilage thickness compartment averaging.

SUBMITTER: Morales AG

PROVIDER: S-EPMC8578418 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Uncovering associations between data-driven learned qMRI biomarkers and chronic pain.

Morales Alejandro G AG Lee Jinhee J JJ Caliva Francesco F Iriondo Claudia C Liu Felix F Majumdar Sharmila S Pedoia Valentina V

Scientific reports 20211109 1

Knee pain is the most common and debilitating symptom of knee osteoarthritis (OA). While there is a perceived association between OA imaging biomarkers and pain, there are weak or conflicting findings for this relationship. This study uses Deep Learning (DL) models to elucidate associations between bone shape, cartilage thickness and T<sub>2</sub> relaxation times extracted from Magnetic Resonance Images (MRI) and chronic knee pain. Class Activation Maps (Grad-CAM) applied on the trained chronic ...[more]

PMID: 34753963

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Project description:BackgroundMounting evidence highlights the importance of combined modifiable lifestyle factors in reducing risk of cognitive decline and dementia. Several a priori additive scoring approaches have been established; however, limited research has employed advanced data-driven approaches to explore this association. This study aimed to examine the association between data-driven lifestyle profiles and cognitive function in community-dwelling Australian adults.MethodsA cross-sectional study of 4561 Australian adults (55.3% female, mean age 60.9 ± 11.3 years) was conducted. Questionnaires were used to collect self-reported data on diet, physical activity, sedentary time, smoking status, and alcohol consumption. Cognitive testing was undertaken to assess memory, processing speed, and vocabulary and verbal knowledge. Latent Profile Analysis (LPA) was conducted to identify subgroups characterised by similar patterns of lifestyle behaviours. The resultant subgroups, or profiles, were then used to further explore associations with cognitive function using linear regression models and an automatic Bolck, Croon & Hagenaars (BCH) approach.ResultsThree profiles were identified: (1) "Inactive, poor diet" (76.3%); (2) "Moderate activity, non-smokers" (18.7%); and (3) "Highly active, unhealthy drinkers" (5.0%). Profile 2 "Moderate activity, non-smokers" exhibited better processing speed than Profile 1 "Inactive, poor diet". There was also some evidence to suggest Profile 3 "Highly active, unhealthy drinkers" exhibited poorer vocabulary and verbal knowledge compared to Profile 1 and poorer processing speed and memory scores compared to Profile 2.ConclusionIn this population of community-dwelling Australian adults, a sub-group characterised by moderate activity levels and higher rates of non-smoking had better cognitive function compared to two other identified sub-groups. This study demonstrates how LPA can be used to highlight sub-groups of a population that may be at increased risk of dementia and benefit most from lifestyle-based multidomain intervention strategies.

Dataset Information

Uncovering associations between data-driven learned qMRI biomarkers and chronic pain.

Publications

Uncovering associations between data-driven learned qMRI biomarkers and chronic pain.

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