Computational NeuroscienceHigh-throughput optogenetic functional magnetic resonance imaging with parallel computations

- A system enabling fast, high-throughput ofMRI studies is proposed.
- Highly optimized and massively parallel algorithms process 3D images in 12.80 ms.
- Enabled future real-time integration of advanced reconstruction and automatic registration.
- Higher effective temporal resolution is demonstrated by detection of initial dips.
- Proposed faster and more accurate parallel motion correction algorithm.

Optogenetic functional magnetic resonance imaging (ofMRI) technology enables cell-type-specific, temporally precise neuronal control and the accurate, in vivo readout of the resulting activity across the entire brain. With the ability to precisely control excitation and inhibition parameters and accurately record the resulting activity, there is an increased need for a high-throughput method to bring the ofMRI studies to their full potential. In this paper, an advanced system facilitating real-time fMRI with interactive control and analysis in a fraction of the MRI acquisition repetition time (TR) is proposed. With high-processing speed, sufficient time will be available for the integration of future developments that further enhance ofMRI data or streamline the study. We designed and implemented a highly optimised, massively parallel system using graphics processing units (GPUs), which achieves the reconstruction, motion correction, and analysis of 3D volume data in approximately 12.80 ms. As a result, with a 750 ms TR and 4 interleaf fMRI acquisition, we can now conduct sliding window reconstruction, motion correction, analysis and display in approximately 1.7% of the TR. Therefore, a significant amount of time can now be allocated to integrating advanced but computationally intensive methods that improve image quality and enhance the analysis results within a TR. Utilising the proposed high-throughput imaging platform with sliding window reconstruction, we were also able to observe the much-debated initial dips in our ofMRI data. Combined with methods to further improve SNR, the proposed system will enable efficient real-time, interactive, high-throughput ofMRI studies.