Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even superseded the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS). MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients and healthy controls (n = 147). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of..

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even surpassed the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on 3D convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS), the most widespread autoimmune neuroinflammatory disease. MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients (n = 76) and healthy controls (n = 71). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of CNN models transparent, which could serve to justify classification decisions for clinical review, verify diagnosis-relevant features and potentially gather new disease knowledge

Associations Between Cardiorespiratory Fitness, Adiposity, and White Matter Integrity

White matter (WM) is essential for transmitting neural signal between brain regions, and supporting healthy brain aging and cognitive function. Risk for WM deterioration is heightened in overweight and obesity, whereas increasing cardiorespiratory fitness may promote WM integrity. However, there is a lack of research comparing adiposity and cardiorespiratory fitness with WM. Further, it is not clear whether increasing cardiorespiratory fitness may outweigh the influence of excess adiposity on WM integrity in middle adulthood. In a sample of adults with overweight and obesity, we examined whether cardiorespiratory fitness and adiposity associate with WM integrity, both independently and jointly. We assessed WM pathways sensitive to cardiorespiratory fitness, adiposity, or both, and tested potential interactions. Baseline data from 125 middle-aged participants (Mage = 44.33 ± 8.60), with overweight or obesity (MBMI = 32.45 ± 4.19), were included in the study. Fitness was assessed via a submaximal graded exercise test. To quantify adiposity, whole body estimates of body fat % were calculated using dual-energy X-ray absorptiometry. Diffusion weighted images were acquired during an MRI protocol. We conducted whole-brain voxelwise analyses using the FMRIB’s Software Library randomise function to examine main effects of adiposity and fitness, as well as the interaction term, on WM integrity. After controlling for age, gender, and years of education, there were no significant main effects of adiposity or cardiorespiratory fitness on FA (all p > .05). There was a significant interaction (p = .03) such that with higher fitness levels, greater adiposity was associated with higher WM integrity, whereas with lower fitness levels greater adiposity was negatively associated with WM integrity. This pattern of findings was unexpected, and may be a function of the unique nature of the sample or related to the confounding effects of WM lesions or local inflammation. Future work may focus on accounting for the influence of WM lesions, and extending the analysis to older adults and patient populations