Multisite Comparison of MRI Defacing Software Across Multiple Cohorts.

With improvements to both scan quality and facial recognition software, there is an increased risk of participants being identified by a 3D render of their structural neuroimaging scans, even when all other personal information has been removed. To prevent this, facial features should be removed before data are shared or openly released, but while there are several publicly available software algorithms to do this, there has been no comprehensive review of their accuracy within the general population. To address this, we tested multiple algorithms on 300 scans from three neuroscience research projects, funded in part by the Ontario Brain Institute, to cover a wide range of ages (3-85 years) and multiple patient cohorts. While skull stripping is more thorough at removing identifiable features, we focused mainly on defacing software, as skull stripping also removes potentially useful information, which may be required for future analyses. We tested six publicly available algorithms (afni_refacer, deepdefacer, mri_deface, mridefacer, pydeface, quickshear), with one skull stripper (FreeSurfer) included for comparison. Accuracy was measured through a pass/fail system with two criteria; one, that all facial features had been removed and two, that no brain tissue was removed in the process. A subset of defaced scans were also run through several preprocessing pipelines to ensure that none of the algorithms would alter the resulting outputs. We found that the success rates varied strongly between defacers, with afni_refacer (89%) and pydeface (83%) having the highest rates, overall. In both cases, the primary source of failure came from a single dataset that the defacer appeared to struggle with - the youngest cohort (3-20 years) for afni_refacer and the oldest (44-85 years) for pydeface, demonstrating that defacer performance not only depends on the data provided, but that this effect varies between algorithms. While there were some very minor differences between the preprocessing results for defaced and original scans, none of these were significant and were within the range of variation between using different NIfTI converters, or using raw DICOM files.

Cardiac-Related Pulsatility in the Insula Is Directly Associated With Middle Cerebral Artery Pulsatility Index.

BACKGROUND: Arterial stiffness in large arteries is a risk factor for cerebral small vessel disease and neurodegeneration. The challenge of accessing intracranial pulsatility noninvasively is one reason few studies provide empirical insight on the relationship between large artery and tissue pulsatility in the human brain. PURPOSE: To investigate the association between the functional magnetic resonance imaging (fMRI)-derived cardiac-related pulsatility in the insular cortex and the ultrasound-derived pulsatility index in the middle cerebral artery (MCA-PI). STUDY TYPE: Cross-sectional. POPULATION: Younger adults (11; 25 ± 4 years) and older adults with and without cardiovascular risk factors (44; 70 ± 6 years). FIELD STRENGTH/SEQUENCE: T1 -weighted, fluid attenuated inversion recovery, and T2 *-weighted blood oxygenation level-dependent (BOLD) sequences at 3T. ASSESSMENT: MCA-PI and cardiac-related pulsatility were assessed at rest by transcranial Doppler ultrasound and BOLD fMRI, respectively. STATISTICAL TESTS: Multivariate analyses of covariance between MCA-PI and cardiac-related pulsatility. Analysis of variance was used to assess regional differences. RESULTS: MCA-PI was associated with cardiac-related insular pulsatility (P = 0.037), but not whole-brain pulsatility (P = 0.81). Left insular pulsatility was higher than right insular pulsatility (P < 0.01) and was associated with diastolic blood pressure (P = 0.028). DATA CONCLUSION: We show a correlation between ultrasound and fMRI measures of cerebrovascular pulsatility. This association provides insight into the transmission of pulsatile energy from large basal arteries at the Circle of Willis to downstream cerebrovascular beds and has implications for the utility of cardiac-related pulsatility as a potential marker for cerebral small vessel disease. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:1454-1462.

Cerebrovascular blood oxygenation level dependent pulsatility at baseline and following acute exercise among healthy adolescents.

Arterial stiffness is linked to cerebral small vessel damage and neurodegeneration, but barriers to accessing deep cerebrovascular anatomy limit our ability to assess the brain. This study describes an adaptation of a cardiac-related scrubbing method as a means of generating blood oxygenation level-dependent pulsatility maps based on the cardiac cycle. We examine BOLD pulsatility at rest, based on the non-parametric deviation from null metric, as well as changes following acute physiological stress from 20 min of moderate-intensity cycling in 45 healthy adolescents. We evaluate the influence of repetition time (TR) and echo time (TE) using simulated and multi-echo empirical data, respectively. There were tissue-specific and voxel-wise BOLD pulsatility decreases 20 min following exercise cessation. BOLD pulsatility detection was comparable over a range of TR and TE values when scan volumes were kept constant; however, short TRs (≤500 ms) and TEs (∼14 ms) acquisitions would yield the most efficient detection. Results suggest cardiac-related BOLD pulsatility may represent a robust and easily adopted method of mapping cerebrovascular pulsatility with voxel-wise resolution.