A Review on Advances in Intra-operative Imaging for Surgery and Therapy: Imagining the Operating Room of the Future

none4openZaffino, Paolo; Moccia, Sara; De Momi, Elena; Spadea, Maria FrancescaZaffino, Paolo; Moccia, Sara; De Momi, Elena; Spadea, Maria Francesc

Getting the best outcomes from epilepsy surgery

Neurosurgery is an under-utilised treatment that can potentially cure drug-refractory epilepsy. Careful, multidisciplinary pre-surgical evaluation is vital for selecting patients and ensure optimal outcomes. Advances in neuroimaging have improved diagnosis and guide surgical intervention. Invasive electroencephalography allows the evaluation of complex patients who would otherwise not be candidates for neurosurgery. We review the current state of the assessment and selection of patients and consider established and novel surgical procedures, and associated outcome data. We aim to dispel myths that may inhibit physicians from referring and patients from considering neurosurgical intervention for drug-refractory focal epilepsies

Representation Learning With Convolutional Neural Networks

Deep learning methods have achieved great success in the areas of Computer Vision and Natural Language Processing. Recently, the rapidly developing field of deep learning is concerned with questions surrounding how we can learn meaningful and effective representations of data. This is because the performance of machine learning approaches is heavily dependent on the choice and quality of data representation, and different kinds of representation entangle and hide the different explanatory factors of variation behind the data. In this dissertation, we focus on representation learning with deep neural networks for different data formats including text, 3D polygon shapes, and brain fiber tracts. First, we propose a topic-based word representation learning approach for text classification. The proposed approach takes global semantic relationship between words over the whole corpus into consideration and encodes the relationships into distributed vector representations with continuous Skip-gram model. The learned representations which capture a large number of precise syntactic and semantic word relationships are taken as input of Convolution Neural Networks for classification. Our experimental results show the effectiveness of the proposed method on indexing of biomedical articles, behavior code annotation of clinical text fragments, and classification of news groups. Second, we present a 3D polygon shape representation learning framework for shape segmentation. We propose Directionally Convolutional Network (DCN) that extends convolution operations from images to the polygon mesh surface with rotation-invariant property. Based on the proposed DCN, we learn effective shape representations from raw geometric features and then classify each face of a given polygon into predefined semantic parts. Through extensive experiments, we demonstrate that our framework outperforms the current state-of-the-arts. Third, we propose to learn effective and meaningful representations for brain fiber tracts using deep learning frameworks. We handle the highly unbalanced dataset by introducing asymmetrical loss function for easily classified samples and hard classified ones. The training loss avoids to be dominated by the easy samples and the training step is more efficient. In addition, we learn more effective and meaningful representations by introducing deeper network and metric learning approaches. Furthermore, we propose to improve the interpretability of our framework by inducing attention mechanism. Our experimental results show that our proposed framework outperforms current golden standard significantly on the real-world dataset

The role of neuronavigation in TMS-EEG studies : Current applications and future perspectives

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) allows measuring noninvasively the electrical response of the human cerebral cortex to a direct perturbation. Complementing TMSEEG with a structural neuronavigation tool (nTMS-EEG) is key for accurately selecting cortical areas, targeting them, and adjusting the stimulation parameters based on some relevant anatomical priors. This step, together with the employment of visualization tools designed to perform a quality check of TMS-evoked potentials (TEPs) in real-time during TMS-EEG data acquisition, is pivotal for maximizing the impact of the TMS pulse on the cortex and in ensuring highly reproducible measurements within sessions and across subjects. Moreover, storing stimulation parameters in the neumnavigation system can help in replicating the stimulation parameters within and across experimental sessions and sharing them across research centers. Finally, the systematic employment of neumnavigation in TMS-EEG studies is also critical to standardize measurements in clinical populations in search for reliable diagnostic and prognostic TMS-EEG-based biomarkers for neurological and psychiatric disorders.Peer reviewe