RESUMO
Resting state functional magnetic resonance imaging (rs-fMRI) has become an indispensable tool in neuroscience research. Despite this, rs-fMRI signals are easily contaminated by artifacts arising from movement of the head during data collection. The artifacts can be problematic even for motions on the millimeter scale, with complex spatiotemporal properties that can lead to substantial errors in functional connectivity estimates. Effective correction methods must be employed, therefore, to distinguish true functional networks from motion-related noise. Research over the last three decades has produced numerous correction methods, many of which must be applied in combination to achieve satisfactory data quality. Subject instruction, training, and mild restraints are helpful at the outset, but usually insufficient. Improvements come from applying multiple motion correction algorithms retrospectively after rs-fMRI data are collected, although residual artifacts can still remain in cases of elevated motion, which are especially prevalent in patient populations. Although not commonly adopted at present, "real-time" correction methods are emerging that can be combined with retrospective methods and that promise better correction and increased rs-fMRI signal sensitivity. While the search for the ideal motion correction protocol continues, rs-fMRI research will benefit from good disclosure practices, such as: (1) reporting motion-related quality control metrics to provide better comparison between studies; and (2) including motion covariates in group-level analyses to limit the extent of motion-related confounds when studying group differences.
RESUMO
BACKGROUND: Brain tumor surgery requires careful balance between maximizing tumor excision and preserving eloquent cortex. In some cases, the surgeon may opt to perform an awake craniotomy including intraoperative mapping of brain function by direct cortical stimulation (DCS) to assist in surgical decision-making. Preoperatively, functional magnetic resonance imaging (fMRI) facilitates planning by identification of eloquent brain areas, helping to guide DCS and other aspects of the surgical plan. However, brain deformation (shift) limits the usefulness of preoperative fMRI during surgery. To address this, an integrated visualization method for fMRI and DCS results is developed that is intuitive for the surgeon. METHODS: An image registration pipeline was constructed to display preoperative fMRI data corrected for brain shift overlaid on images of the exposed cortical surface at the beginning and completion of DCS mapping. Preoperative fMRI and DCS data were registered for a range of misalignments, and the residual registration errors were calculated. The pipeline was validated on imaging data from five brain tumor patients who underwent awake craniotomy. RESULTS: Registration errors were well under 5 mm (the approximate spatial resolution of DCS) for misalignments of up to 25 mm and approximately 10-15°. For rotational misalignments up to 20°, the success rate was 95% for an error tolerance of 5 mm. Failures were negligible for rotational misalignments up to 10°. Good quality registrations were observed for all five patients. CONCLUSIONS: A proof-of-concept image registration pipeline is presented with acceptable accuracy for intraoperative use, providing multimodality visualization with potential benefits for intraoperative brain mapping.
