Scholarship & Research
Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/1
Browse
2 results
Search Results
Item Exploring the feasibility of an automated biocuration pipeline for research domain criteria(Montana State University - Bozeman, College of Engineering, 2019) Anani, Mohammad; Chairperson, Graduate Committee: Indika KahandaResearch on mental disorders has been largely based on manuals such as the ICD-10 (International Classification of Diseases) and DSM-V (the Diagnostic Statistical Manual of Mental Disorders), which rely on the signs and symptoms of disorders for classification. However, this approach tends to overlook the underlying mechanisms of brain disorders and does not express the heterogeneity of those conditions. Thus, the National Institute of Mental Health (NIMH) introduced a new framework for mental illness research, namely, Research Domain Criteria (RDoC). RDoC is a research framework which utilizes various units of analysis from genetics, neural circuits, etc., for accurate multi-dimensional classification of mental illnesses. The RDoC framework is manually updated with units of analysis in periodic workshops. The process of updating the RDoC framework is accomplished by researching relevant evidence in the literature by domain experts. Due to the large amount of relevant biomedical research available, developing a method to automate the process of extracting evidence from the biomedical literature to assist with the curation of the RDoC matrix is key. In this thesis, we formulate three tasks that would be necessary for an automated biocuration pipeline for RDoC: 1) Labeling biomedical articles with RDoC constructs, 2) Retrieval of brain research articles, and 3) Extraction of relevant data from these articles. We model the first problem as a multilabel classification problem with 26 constructs of RDoC and use a gold-standard dataset of annotated PubMed abstracts and employ various supervised classification algorithms. The second task classifies general PubMed abstracts relevant to brain research using the same data from the first task and other unlabeled abstracts for training a model. Finally, for the third task, we attempt to extract Problem, Intervention, Comparison, and Outcomes (PICO) elements and brain region mentions from a subset of the RDoC abstracts. To the best of our knowledge, this is the first study aimed at automated data extraction and retrieval of RDoC related literature. The results of automating the aforementioned tasks are promising; we have a very accurate multilabel classification model, a good retrieval model, and an accurate brain region extraction model.Item Design and implementation of a real-time system to characterize functional connectivity between cortical areas(Montana State University - Bozeman, College of Engineering, 2017) Parsa Gharamaleki, Mohammadbagher; Chairperson, Graduate Committee: Brendan MumeyDespite a thorough mapping of the anatomical connectivity between brain regions and decades of neurophysiological studies of neuronal activity within the various areas, our understanding of the nature of the neural signals sent from one area to another remains rudimentary. Orthodromic and antidromic activation of neurons via electrical stimulation ('collision testing') has been used in the peripheral nervous system and in subcortical structures to identify signals propagating along specific neural pathways. However, low yield makes this method prohibitively slow for characterizing cortico-cortical connections. We employed recent advances in electrophysiological methods to improve the efficiency of the collision technique between cortical areas. There are three key challenges: 1) maintaining neuronal isolations following stimulation, 2) increasing the number of neurons being screened, and 3) ensuring low-latency triggering of stimulation after spontaneous action potentials. We have developed a software-hardware solution for online isolations and stimulation triggering, which operates in conjunction with two hardware options, Hardware Processing Platform (HPP) or a Software Processing Platform (SPP). The HPP is a 'system on a chip' solution enabling real-time processing in a re-programmable hardware platform, whereas the SPP is a small Intel Atom processor that allows soft real-time computing on a CPU. Employing these solutions for template matching both accelerates spike sorting and provides the low-latency triggering of stimulation required to produce collision trials. Recording with a linear tetrode array electrode allows simultaneous screening of multiple neurons, while the software package coordinates efficient collision testing of multiple user-selected units across channels. This real-time connectivity screening system enables researchers working with a variety of animal models and brain regions to identify the functional properties of specific projections between cortical areas in behaving animals.