Biology subjects

Hallett, M.

Publications and source records attributed to Hallett, M..

3 recordsLinked to original sources

bioRxiv2018

Modulation of Resting Connectivity Between the Mesial Frontal Cortex and Basal Ganglia

The mesial prefrontal cortex, cingulate cortex and the ventral striatum are key nodes of the human mesial fronto-striatal circuit involved in decision-making and executive function and pathological disorders. Here we ask whether deep wide-field repetitive transcranial magnetic stimulation (rTMS) targeting the mesial prefrontal cortex (MPFC) influences resting state functional connectivity. In Study 1, we examined functional connectivity using resting state multi-echo and independent components analysis in 154 healthy subjects to characterize default connectivity in the MPFC and mid-cingulate cortex (MCC). In Study 2, we used inhibitory, 1 Hz deep rTMS with the H7-coil targeting MPFC and dorsal anterior cingulate (dACC) in a separate group of 20 healthy volunteers and examined pre-and post-TMS functional connectivity using seed-based and independent components analysis. In Study 1, we show that MPFC and MCC have distinct patterns of functional connectivity with MPFC-ventral striatum showing negative, whereas MCC-ventral striatum showing positive functional connectivity. Low-frequency rTMS decreased functional connectivity of MPFC and dACC with the ventral striatum. We further showed enhanced connectivity between MCC and ventral striatum. These findings emphasize how deep inhibitory rTMS using the H7-coil can influence underlying network functional connectivity by decreasing connectivity of the targeted MPFC regions, thus potentially enhancing response inhibition and decreasing drug cue reactivity processes relevant to addictions. The unexpected finding of enhanced default connectivity between MCC and ventral striatum may be related to the decreased influence and connectivity between the MPFC and MCC. These findings are highly relevant to the treatment of disorders relying on the mesioprefrontal-cingulo-striatal circuit.

neuroscience↗

bioRxiv2018

Reproducible Data Analysis Pipelines for Precision Medicine

Precision medicine brings the promise of more precise diagnosis and individualized therapeutic strategies from analyzing a cancers genomic signature. Technologies such as high-throughput sequencing enable cheaper data collection at higher speed, but rely on modern data analysis platforms to extract knowledge from these high dimensional datasets. Since this is a rapidly advancing field, new diagnoses and therapies often require tailoring of the analysis. These pipelines are therefore developed iteratively, continuously modifying analysis parameters before arriving at the final results. To enable reproducible results it is important to record all these modifications and decisions made during the analysis process.\n\nWe built a system, walrus, to support reproducible analyses for iteratively developed analysis pipelines. The approach is based on our experiences developing and using deep analysis pipelines to provide insights and recommendations for treatment in an actual breast cancer case. We designed walrus for the single servers or small compute clusters typically available for novel treatments in the clinical setting. walrus leverages software containers to provide reproducible execution environments, and integrates with modern version control systems to capture provenance of data and pipeline parameters.\n\nWe have used walrus to analyze a patients primary tumor and adjacent normal tissue, including subsequent metastatic lesions. Although we have used walrus for specialized analyses of whole-exome sequencing datasets, it is a general data analysis tool that can be applied in a variety of scientific disciplines. We have open sourced walrus along with example data analysis pipelines at github.com/uit-bdps/walrus.

bioinformatics↗

bioRxiv2017

Building Applications For Interactive Data Exploration In Systems Biology

As the systems biology community generates and collects data at an unprecedented rate, there is a growing need for interactive data exploration tools to explore the datasets. These tools need to combine advanced statistical analyses, relevant knowledge from biological databases, and interactive visualizations in an application with clear user interfaces. To answer specific research questions tools must provide specialized user interfaces and visualizations. While these are application-specific, the underlying components of a data analysis tool can be shared and reused later. Application developers can therefore compose applications of reusable services rather than implementing a single monolithic application from the ground up for each project.\n\nOur approach for developing data exploration applications in systems biology builds on the microservice architecture. Microservice architectures separates an application into smaller components that communicate using language-agnostic protocols. We show that this design is suitable in bioinformatics applications where applications often use different tools, written in different languages, by different research groups. Packaging each service in a software container enables re-use and sharing of key components between applications, reducing development, deployment, and maintenance time.\n\nWe demonstrate the viability of our approach through a web application, MIxT blood-tumor, for exploring and comparing transcriptional profiles from blood and tumor samples in breast cancer patients. The application integrates advanced statistical software, up-to-date information from biological databases, and modern data visualization libraries.\n\nThe web application for exploring transcriptional profiles, MIxT, is online at mixt-blood-tumor.bci.mcgill.ca and open-sourced at github.com/fjukstad/mixt. Packages to build the supporting microservices are open-sourced as a part of Kvik at github.com/fjukstad/kvik.

bioinformatics↗