Improving Sensitivity to Functional Responses without a Loss of Spatiotemporal Precision in Human Brain Imaging

As the neuroimaging field moves towards detecting smaller effects at higher spatial resolutions, and faster sampling rates, there is increased attention given to the deleterious contribution of unstructured, thermal noise. Here, we critically evaluate the performance of a recently developed reconstruction method, termed NORDIC, for suppressing thermal noise using datasets acquired with various field strengths, voxel sizes, sampling rates, and task designs.Following minimal preprocessing, statistical activation (t-values) of NORDIC processed data was compared to the results obtained with alternative denoising methods. Additionally, the consistency of unbiased estimations of task responses at the single-voxel, single run level, using a finite impulse response model were evaluated. To examine the potential impact on effective image resolution, the overall smoothness of the data processed with different methods was examined. Finally, to determine if NORDIC alters or removes important temporal information, an exhaustive K-fold cross validation approach was employed, using unbiased task responses to predict held out timeseries, quantified using R2.After NORDIC, the t-values are increased, an improvement comparable to what could be achieved by 1.5 voxels smoothing, and task events are clearly visible and have less cross-run error. These advantages are achieved without significant compromises in spatial and temporal resolution. Cross-validated R2s based on the unbiased models show that NORDIC is not distorting the temporal structure of the data and is the best predictor of non-denoised time courses. The results demonstrate that 1 run of NORDIC data is equivalent to using 2 to 3 original runs, and performs equally well across a diverse array of functional imaging protocols.For functional neuroimaging, the increasing availability of higher field strengths and ever higher spatiotemporal resolutions has led to concomitant increase in concerns about the deleterious effects of thermal noise. Historically this noise source was suppressed using methods that reduce spatial precision such as image blurring or averaging over a large number of trials or sessions, which necessitates large data collection efforts. Here, we critically evaluate the performance of a recently developed reconstruction method, termed NORDIC. Across datasets varying in field strength, voxel sizes, sampling rates, and task designs, NORDIC produces substantial gains in data quality. Both conventional t-statistics derived from general linear models and coefficients of determination for predicting unseen data are improved, without notably increasing image smoothness.