Multimodal MRI analysis using deep learning methods

Magnetic resonance imaging (MRI) has been widely used in scientific and clinical research. It is a non-invasive medical imaging technique that reveals anatomical structures and provides useful information for investigators to explore aging and pathological processes. Different MR modalities offer different useful properties. Automatic MRI analysis algorithms have been developed to address problems in many applications such as classification, segmentation, and disease diagnosis. Segmentation and labeling algorithms applied to brain MRIs enable evaluations of the volumetric changes of specific structures in neurodegenerative diseases. Reconstruction of fiber orientations using diffusion MRI is beneficial to obtain better understanding of the underlying structures. In this thesis, we focused on development of deep learning methods for MRI analysis using different image modalities. Specifically, we applied deep learning techniques on different applications, including segmentation of brain structures and reconstruction of tongue muscle fiber orientations. For segmentation of brain structures, we developed an end-to-end deep learning algorithm for ventricle parcellation of brains with ventriculomegaly using T1-w MR images. The deep network provides robust and accurate segmentation results in subjects with high variability in ventricle shapes and sizes. We developed another deep learning method to automatically parcellate the thalamus into a set of thalamic nuclei using T1-w MRI and features from diffusion MRI. The algorithm incorporates a harmonization step to make the network adapt to input images with different contrasts. We also studied the strains associated with tongue muscles during speech production using multiple MRI modalities. To enable this study, we first developed a deep network to reconstruct crossing tongue muscle fiber orientations using diffusion MRI. The network was specifically designed for the human tongue and accounted for the orthogonality property of the tongue muscles. Next, we proposed a comprehensive pipeline to analyze the strains associated with tongue muscle fiber orientations during speech using diffusion MRI, and tagged and cine MRI. The proposed pipeline provides a solution to analyze the cooperation between muscle groups during speech production

Paraneoplastic Antibodies

Paraneoplastic neurological syndromes (PNS) are remote effects of cancer. These are not caused by invasion of the tumor or its metastasis nor by other direct effects of the tumor or its treatment. PNS are rare, affecting less than 0.1% of all cancer patients. PNS have a subacute course, leaving the patient severely disabled in weeks to months. In most cases PNS precede the diagnosis of cancer. The discovery of paraneoplastic antineuronal antibodies facilitated the diagnosis. ‘Well characterized onconeural antibodies’ are defined by recognizable patterns on rat brain immunohistochemistry and positive immunoblotting on recombinant antigen proteins. These are exclusively found in patients with cancer and include anti-Hu, Yo, CV2, Ri, Ma2 and amphiphysin. These antigens represent intracellular proteins, so in PNS damage is caused by cellular immune responses, explaining the poor response to immune modulating treatment and poor prognosis. We identified the anti-Tr antigen as the transmembrane protein Delta/ Notch-like epidermal growth factor-related receptor (DNER). More recently a still growing number of autoantibodies directed against synaptic or neuronal cell-surface antigens has been identified, including mGluR1, NMDA, AMPA and GABA receptors. These autoantibodies have direct access to their target antigen and are potentially pathogenic. The associated clinical syndromes may be paraneoplastic or may represent an autoimmune encephalitis (without underlying tumor). Patients harboring autoantibodies directed against synaptic or neuronal cell-surface antigens respond favorably to immunotherapy with a good outcome in up to 80%. This thesis focuses on paraneoplastic antineuronal antibodies and includes studies on new methods of autoantibody detection, identification of novel paraneoplastic antigen(s) and the description of clinical syndromes associated with newly detected paraneoplastic antibodies

Translational and clinical research applications of exome sequencing to neurodevelopmental disorders of childhood

Neurodevelopmental disabilities (NDDs) are a group of chronic clinically distinct disorders sharing a documented disturbance, quantitative, qualitative, or both, in developmental progress in one or more developmental domains compared with established norms. These domains are not mutually independent or exclusive and include: (1) motor (gross or fine), (2) speech and language, (3) cognition, (4) personal-social, and (5) activities of daily living (Shevell et al., 2008). Whole Exome Sequencing (WES) has provided a huge contribution to the discovery of disease-causing variants for rare diseases, especially neurodevelopmental disorders. Furthemore, the use of WES in clinical practice has improved the diagnostic rate in several rare genetic conditions, including neurodevelopmental disorders, which previously remained unexplained (Xue et al., 2014). This has led to a relevant improvement in patient management in selected disorders. Indeed, the better understanding of the pathophysiology underlying a specific condition has helped clinicians in developing a disease-specific approach in patient care. Furthermore, the identification of several new possible therapeutic targets has promoted the development of new therapeutic strategies or specific drugs. In this study, we investigated the use of exome sequencing in three different genomic research approaches: genotype-phenotype correlations; pathophysiological mechanisms; gene discovery. Our findings show that NGS techniques play a pivotal role in the NDDs research

Pan European Voice Conference - PEVOC 11

The Pan European VOice Conference (PEVOC) was born in 1995 and therefore in 2015 it celebrates the 20th anniversary of its establishment: an important milestone that clearly expresses the strength and interest of the scientific community for the topics of this conference. The most significant themes of PEVOC are singing pedagogy and art, but also occupational voice disorders, neurology, rehabilitation, image and video analysis. PEVOC takes place in different European cities every two years (www.pevoc.org). The PEVOC 11 conference includes a symposium of the Collegium Medicorum Theatri (www.comet collegium.com