Creating a pipeline for functional connectivity analysis in fNIRS motor control studies
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Functional near-infrared spectroscopy (fNIRS) has shown to be a useful imaging technique for observing the human cortex. Its capabilities of imaging the hemodynamic variations in the cerebral cortex during different experimental paradigms enable studies that can contribute to unraveling the orchestration of the mind. With many questions regarding its workings still unanswered, different techniques are being employed in the analysis of fNIRS data to illuminate activation patterns and correlations between brain areas during different tasks. One technique that is gotten more attention during the last years, is functional connectivity analysis which aims to assess the temporal correlations between these areas. With the potential of assessing the cortical connectivity, this technique can contribute to analyzing people with different health challenges, both regarding mental and motor health. However, with a lack of standards in pre-processing and processing techniques the analysis of such studies is known to be time-consuming and challenging. The different processing software and toolboxes are taking different approaches to perform these steps and with little insight into how the data is processed during these steps, a consistent and transparent pipeline is desired to enable reliable results in fNIRS motor control studies. This has led to the aim of mapping the most promising techniques used in pre-processing and processing of motor control fNIRS studies and implementing these techniques in an automatic processing pipeline. Thus, the objective of this study has been to develop such a pipeline to facilitate faster and more consistent functional connectivity analysis. The developed pipeline is thoroughly described in this thesis, and its workings are presented using data from an ongoing motor control study.