Immediate brain plasticity after one hour of brain-computer interface (BCI).

KEY POINTS: Two groups of inexperienced brain-computer interface users underwent a purely mental EEG-BCI session that rapidly impacted on their brain. Modulations in structural and functional MRI were found after only 1 h of BCI training. Two different types of BCI (based on motor imagery or visually evoked potentials) were employed and analyses showed that the brain plastic changes are spatially specific for the respective neurofeedback. This spatial specificity promises tailored therapeutic interventions (e.g. for stroke patients). ABSTRACT: A brain-computer-interface (BCI) allows humans to control computational devices using only neural signals. However, it is still an open question, whether performing BCI also impacts on the brain itself, i.e. whether brain plasticity is induced. Here, we show rapid and spatially specific signs of brain plasticity measured with functional and structural MRI after only 1 h of purely mental BCI training in BCI-naive subjects. We employed two BCI approaches with neurofeedback based on (i) modulations of EEG rhythms by motor imagery (MI-BCI) or (ii) event-related potentials elicited by visually targeting flashing letters (ERP-BCI). Before and after the BCI session we performed structural and functional MRI. For both BCI approaches we found increased T1-weighted MR signal in the grey matter of the respective target brain regions, such as occipital/parietal areas after ERP-BCI and precuneus and sensorimotor regions after MI-BCI. The latter also showed increased functional connectivity and higher task-evoked BOLD activity in the same areas. Our results demonstrate for the first time that BCI by means of targeted neurofeedback rapidly impacts on MRI measures of brain structure and function. The spatial specificity of BCI-induced brain plasticity promises therapeutic interventions tailored to individual functional deficits, for example in patients after stroke.

Mammography Image Quality Assurance Using Deep Learning.

OBJECTIVE: According to the European Reference Organization for Quality Assured Breast Cancer Screening and Diagnostic Services (EUREF) image quality in mammography is assessed by recording and analyzing a set of images of the CDMAM phantom. The EUREF procedure applies an automated analysis combining image registration, signal detection and nonlinear fitting. We present a proof of concept for an end-to-end deep learning framework that assesses image quality on the basis of single images as an alternative. METHODS: Virtual mammography is used to generate a database with known ground truth for training a regression convolutional neural net (CNN). Training is carried out by continuously extending the training data and applying transfer learning. RESULTS: The trained net is shown to correctly predict the image quality of simulated and real images. Specifically, image quality predictions on the basis of single images are of similar quality as those obtained by applying the EUREF procedure with 16 images. Our results suggest that the trained CNN generalizes well. CONCLUSION: Mammography image quality assessment can benefit from the proposed deep learning approach. SIGNIFICANCE: Deep learning avoids cumbersome pre-processing and allows mammography image quality to be estimated reliably using single images.