A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

BackgroundThe clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal.Methodology/Principal FindingsNon-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification.Conclusions/SignificanceWe show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing

pitx2 Deficiency Results in Abnormal Ocular and Craniofacial Development in Zebrafish

Human PITX2 mutations are associated with Axenfeld-Rieger syndrome, an autosomal-dominant developmental disorder that involves ocular anterior segment defects, dental hypoplasia, craniofacial dysmorphism and umbilical abnormalities. Characterization of the PITX2 pathway and identification of the mechanisms underlying the anomalies associated with PITX2 deficiency is important for better understanding of normal development and disease; studies of pitx2 function in animal models can facilitate these analyses. A knockdown of pitx2 in zebrafish was generated using a morpholino that targeted all known alternative transcripts of the pitx2 gene; morphant embryos generated with the pitx2ex4/5 splicing-blocking oligomer produced abnormal transcripts predicted to encode truncated pitx2 proteins lacking the third (recognition) helix of the DNA-binding homeodomain. The morphological phenotype of pitx2ex4/5 morphants included small head and eyes, jaw abnormalities and pericardial edema; lethality was observed at ∼6–8-dpf. Cartilage staining revealed a reduction in size and an abnormal shape/position of the elements of the mandibular and hyoid pharyngeal arches; the ceratobranchial arches were also decreased in size. Histological and marker analyses of the misshapen eyes of the pitx2ex4/5 morphants identified anterior segment dysgenesis and disordered hyaloid vasculature. In summary, we demonstrate that pitx2 is essential for proper eye and craniofacial development in zebrafish and, therefore, that PITX2/pitx2 function is conserved in vertebrates