Mo Shahdloo

I am a researcher at the MR Physics Group at FMRIB Centre, University of Oxford, working with Dr. Mark Chiew. I am also affiliated with the ICON Lab at Bilkent Univeristy.

I am an engineer by training; my undergraduate degree is in Electrical Engineering (2011), from Amirkabir University, Tehran. I was a software and hardware engineer for a couple of years. Then, I moved to Ankara and got MSc (2017) and PhD (2020), both in EE, from Bilkent University.

My research centers on developing methods to make MR imaging faster and more accurate. Specifically, I am focused on reducing motion artifacts in functional MRI in non-human primates. Also, using fMRI, I develop computational models to investigate representations of visual and auditory natural stimuli in the human brain, and their interactions with attention.
Below, you can have a look at the stuff I am busy doing these days.

Publications

Please use this bibliography library file, if you wish to cite any of the works below.

Articles

Peer Reviewed Abstracts

CV

Here is my recent CV ->

Current Projects

  • Motion-correction for awake non-human primate fMRI
    Functional imaging of awake non-human primates is challenging not only due to their smaller brain size, but also due to excessive local field fluctuations due to animals' motion. Using a novel pulse sequence, efficient reconstruction techniques, and a custom-made multi-channel coil we aim to perform accelerated functional scans of awake monkeys.

  • Trade off between fat-suppression and partial-voluming in bSSFP acquisitions
    Fat-suppression in bSSFP can be achieved using weighted average of in-phase and out-of-phase acquisitions raised to a negative power. We studied the effects of the power parameter on the trade off between fat-suppression and partial voluming under various experimental setups. We devised optimal parameter values meeting the fat-suppression and partial voluming requirements.

  • Temporal Receptive Windows in the Brain Mapped via Deep Language Networks
    Using a natural story listening experimental paradigm, we use computational models based on deep neural networks to study the effect of context integration on the representation of a large dictionary of words across the cortex. We are further interested in studying the attentional modulations of this property across cortex.

  • Attentional Modulations of Action-Category Representation in the Brain
    We use voxelwise models to model the contribution of hundreds of objects and action to semantic representation of natural movies across cortex. We then study the attentional modulation of the semantic representation during action-based visual search.

“Without music, life would be a mistake” ― Friedrich Nietzsche