Kevin Jamey
My research interests are on new technologies for music-based cognitive training in children with neurodevelopmental disorders. I completed my MSc. on music perception abilities in children on the Autism Spectrum (AS) at the University of Montreal under the supervision of Krista L. Hyde. My doctoral thesis is supervised by Simone Dalla Bella and examines the neural correlates of a tablet-based rhythmic training game (RhythmWorkers) on executive functioning and speech in children on the AS. In my free time, I am a singer-songwriter, music producer/live act and I love to create powerful musical experiences where lyrics are inflected for rhythm and melody.
- Provide a full neuroimaging workflow from preprocessing of raw data to visualisation of results
- To explore longitudinal analysis between two treatments in this dataset
- To analyse resting-state networks linked to joing attention (attentional control network)
- To learn the full workflow of neuroimaging from converting raw data to preprocessing to data visualisation
- To set myself up with open science tools for neuroimaging work in the future
- To get familiar with the best practices of shareable open science kits and software
- Git and Github for sharing project
- dcm2bids for converting raw data into NiFTY and a BIDS structure
- fMRIPrep for data preprocessing
- Alliance Canada for computing pre-processing
- Python Packages matplotlib, nilearn, plotly
Dataset was collected during my masters in 2016 at the MNI. I scanned ~50 participants with autism before and after either 12 weeks of music therapy or arts and crafts therapy. The data involves a weighted T1 scan and resting-state fMRI. A previous publication on this data set can be found here.
- project workflow detailed in my GitHub repository
- the bash code I used to run BIDS conversion and fMRIprep
- a markdwon file introducing the project, highlighting the results and a discussion with recommendations