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Treatment selection is becoming increasingly more important in acute ischemic stroke patient care. Clinical variables and radiological image biomarkers (old age, pre-stroke mRS, NIHSS, occlusion location, ASPECTS, among others) have an important role in treatment selection and prognosis. Radiological biomarkers require expert annotation and are subject to inter-observer variability. Recently, Deep Learning has been introduced to reproduce these radiological image biomarkers. Instead of reproducing these biomarkers, in this work, we investigated Deep Learning techniques for building models to directly predict good reperfusion after endovascular treatment (EVT) and good functional outcome using CT angiography images. These models do not require image annotation and are fast to compute. We compare the Deep Learning models to Machine Learning models using traditional radiological image biomarkers. We explored Residual Neural Network (ResNet) architectures, adapted them with Structured Receptive Fields (RFNN) and auto-encoders (AE) for network weight initialization. We further included model visualization techniques to provide insight into the network's decision-making process. We applied the methods on the MR CLEAN Registry dataset with 1301 patients. The Deep Learning models outperformed the models using traditional radiological image biomarkers in three out of four cross-validation folds for functional outcome (average AUC of 0.71) and for all folds for reperfusion (average AUC of 0.65). Model visualization showed that the arteries were relevant features for functional outcome prediction. The best results were obtained for the ResNet models with RFNN. Auto-encoder initialization often improved the results. We concluded that, in our dataset, automated image analysis with Deep Learning methods outperforms radiological image biomarkers for stroke outcome prediction and has the potential to improve treatment selection.

Contains fulltext : 157196.pdf (publisher's version ) (Closed access), Background: Major depressive disorder (MDD) is characterized by abnormalities in both brain structure and function within a frontolimbic network. However, little is known about the relation between structural and functional abnormalities in MDD. Here, we used a multimodal neuroimaging approach to investigate the relation between structural connectivity and functional connectivity within the frontolimbic network. Methods: Eighteen MDD and 24 healthy control subjects were included, of which the integrity of the uncinate fasciculus was assessed that connects the subgenual anterior cingulate cortex (ACC) to the medial temporal lobe (MTL) with diffusion tensor imaging. Furthermore, we assessed the functional connectivity between these brain regions with functional magnetic resonance imaging. Results: The results showed that white matter integrity of the uncinate fasciculus was reduced and that functional connectivity between the subgenual ACC and MTL was enhanced in MDD. Importantly, we identified a negative correlation between uncinate fasciculus integrity and subgenual ACC functional connectivity with the bilateral hippocampus in MDD but not in healthy control subjects. Moreover, this negative structure-function relation in MDD was positively associated with depression severity. Conclusions: These findings suggest that structural abnormalities in MDD are associated with increased functional connectivity between subgenual ACC and MTL and that these changes are concomitant with severity of depressive symptoms. This association indicates that structural abnormalities in MDD contribute to increased functional connectivity within the frontolimbic network.

Contains fulltext : 157196.pdf (publisher's version ) (Closed access), Background: Major depressive disorder (MDD) is characterized by abnormalities in both brain structure and function within a frontolimbic network. However, little is known about the relation between structural and functional abnormalities in MDD. Here, we used a multimodal neuroimaging approach to investigate the relation between structural connectivity and functional connectivity within the frontolimbic network. Methods: Eighteen MDD and 24 healthy control subjects were included, of which the integrity of the uncinate fasciculus was assessed that connects the subgenual anterior cingulate cortex (ACC) to the medial temporal lobe (MTL) with diffusion tensor imaging. Furthermore, we assessed the functional connectivity between these brain regions with functional magnetic resonance imaging. Results: The results showed that white matter integrity of the uncinate fasciculus was reduced and that functional connectivity between the subgenual ACC and MTL was enhanced in MDD. Importantly, we identified a negative correlation between uncinate fasciculus integrity and subgenual ACC functional connectivity with the bilateral hippocampus in MDD but not in healthy control subjects. Moreover, this negative structure-function relation in MDD was positively associated with depression severity. Conclusions: These findings suggest that structural abnormalities in MDD are associated with increased functional connectivity between subgenual ACC and MTL and that these changes are concomitant with severity of depressive symptoms. This association indicates that structural abnormalities in MDD contribute to increased functional connectivity within the frontolimbic network.

Contains fulltext : 157196.pdf (publisher's version ) (Closed access), Background: Major depressive disorder (MDD) is characterized by abnormalities in both brain structure and function within a frontolimbic network. However, little is known about the relation between structural and functional abnormalities in MDD. Here, we used a multimodal neuroimaging approach to investigate the relation between structural connectivity and functional connectivity within the frontolimbic network. Methods: Eighteen MDD and 24 healthy control subjects were included, of which the integrity of the uncinate fasciculus was assessed that connects the subgenual anterior cingulate cortex (ACC) to the medial temporal lobe (MTL) with diffusion tensor imaging. Furthermore, we assessed the functional connectivity between these brain regions with functional magnetic resonance imaging. Results: The results showed that white matter integrity of the uncinate fasciculus was reduced and that functional connectivity between the subgenual ACC and MTL was enhanced in MDD. Importantly, we identified a negative correlation between uncinate fasciculus integrity and subgenual ACC functional connectivity with the bilateral hippocampus in MDD but not in healthy control subjects. Moreover, this negative structure-function relation in MDD was positively associated with depression severity. Conclusions: These findings suggest that structural abnormalities in MDD are associated with increased functional connectivity between subgenual ACC and MTL and that these changes are concomitant with severity of depressive symptoms. This association indicates that structural abnormalities in MDD contribute to increased functional connectivity within the frontolimbic network.

We report on the implementation of a software suite dedicated to the management and analysis of large scale RNAi High Content Screening (HCS). We describe the requirements identified amongst our different users, the supported data flow, and the implemented software. Our system is already supporting productively three different laboratories operating in distinct IT infrastructures. The system was already used to analyze hundreds of RNAi HCS plates.

We report on the implementation of a software suite dedicated to the management and analysis of large scale RNAi High Content Screening (HCS). We describe the requirements identified amongst our different users, the supported data flow, and the implemented software. Our system is already supporting productively three different laboratories operating in distinct IT infrastructures. The system was already used to analyze hundreds of RNAi HCS plates.

Diffusion tensor imaging (DTI) provides insight into the white matter of the human brain, which is affected by schizophrenia. By comparing a patient group to a control group, the DTI-images are on average expected to be different for white matter regions. Principal component analysis (PCA) and linear discriminant analysis (LDA) are used to classify the groups. In this work, the number of principal components is optimised for obtaining the minimal classification error. A robust estimate of this error is computed in a cross-validation framework, using different compositions of the data into a training and a testing set Previously, sequential runs were performed in MATLAB, resulting in long execution times. In this paper we describe an experiment where this application was run on a grid with minimal modifications and user effort. We have adopted a service-based approach that autonomously launches image analysis services onto a campus-wide Condor pool comprising of volunteer resources. This allows high throughput analysis of our data in a dynamic resource pool. The challenge in adopting such an approach comes from the nature of the resources, which change randomly with time and thus require fault tolerance. Through this approach we have reduced the computation time of each dataset from 90 minutes to less than 10. A minimal classification error of 22% was obtained, using 15 principal components.