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

Circadian pacemaker coupling by multi-peptidergic neurons in the cockroach Leucophaea maderae

Lesion and transplantation studies in the cockroach, Leucophaea maderae, have located its bilaterally symmetric circadian pacemakers necessary for driving circadian locomotor activity rhythms to the accessory medulla of the optic lobes. The accessory medulla comprises a network of peptidergic neurons, including pigment-dispersing factor (PDF)-expressing presumptive circadian pacemaker cells. At least three of the PDF-expressing neurons directly connect the two accessory medullae, apparently as a circadian coupling pathway. Here, the PDF-expressing circadian coupling pathways were examined for peptide colocalization by tracer experiments and double-label immunohistochemistry with antisera against PDF, FMRFamide, and Asn13-orcokinin. A fourth group of contralaterally projecting medulla neurons was identified, additional to the three known groups. Group one of the contralaterally projecting medulla neurons contained up to four PDF-expressing cells. Of these, three medium-sized PDF-immunoreactive neurons coexpressed FMRFamide and Asn13-orcokinin immunoreactivity. However, the contralaterally projecting largest PDF neuron showed no further peptide colocalization, as was also the case for the other large PDF-expressing medulla cells, allowing the easy identification of this cell group. Although two-thirds of all PDF-expressing medulla neurons coexpressed FMRFamide and orcokinin immunoreactivity in their somata, colocalization of PDF and FMRFamide immunoreactivity was observed in only a few termination sites. Colocalization of PDF and orcokinin immunoreactivity was never observed in any of the terminals or optic commissures. We suggest that circadian pacemaker cells employ axonal peptide sorting to phase-control physiological processes at specific times of the day

Adherence to respiratory and non-respiratory medication in patients with COPD: Results of the German COSYCONET cohort.

Background: Adherence to COPD medication is often considered to be lower than in other chronic diseases. In view of the frequent comorbidities of COPD, the economic impact of nonadherence and the potential for adverse effects, a direct comparison between the adherence to respiratory and nonrespiratory medication in the same patients seems of particular interest. Objectives: We aimed to investigate the intake of respiratory and nonrespiratory medication in the same patients with COPD and frequent comorbidities. Method: Within the COPD cohort COSYCONET, we contacted 1042 patients, mailing them a list with all medication regarding all their diseases, asking for regular, irregular and nonintake. Results: Valid responses were obtained in 707 patients covering a wide spectrum of drugs. Intake of LABA, LAMA or ICS was regular in 91.9% of patients, even higher for cardiovascular and antidiabetes medication but lower for hyperlipidemia and depression/anxiety medication. Regular intake of respiratory medication did not depend on GOLD groups A-D or grades 1–4, was highest in patients with concomitant cardiovascular disorders and was lowest for concomitant asthma. It was slightly larger for LAMA and LABA administered via combined compared to single inhalers, and lower when similar compounds were prescribed twice. Most differences did not reach statistical significance owing to the overall high adherence. Conclusion: Our results indicate a high adherence to respiratory medication in participants of a COPD cohort, especially in those with cardiovascular comorbidities. Compared to the lower adherence reported in the literature for COPD patients, our observations still suggest some room for improvement, possibly through disease management programs