Ward, Chadwick P.

AbstractBackgroundRecent advances in automatic face recognition have increased the risk that de‐identified research imaging data could be re‐identified from face imagery in brain scans.MethodAn ADNI committee of independent imaging experts evaluated 11 published techniques for face‐deidentification (“de‐facing”) and selected four algorithms (FSL‐UK Biobank, HCP/XNAT, mri_reface, and BIC) for formal testing using 183 longitudinal scans of 61 racially and ethnically diverse ADNI participants, evaluated by their facial feature removal on 3D rendered surfaces (confirming sufficient privacy protection) and by comparing measurements from ADNI routine image analyses on unmodified vs. de‐faced images (confirming negligible side effects on analyses).ResultEffects of the de‐facing methods on FreeSurfer at UCSF (Figure 1‐top), ASHS at University of Pennsylvania (Figure 1 bottom‐left), and longitudinal atrophy at Mayo Clinic (Figure 1 bottom‐right) were comparable with each other and sufficiently small vs. unmodified scans to recommend de‐facing in ADNI‐4. The FSL and mri_reface methods were more thorough in removing facial features, partly because these also removed ears (Figure 2‐top). mri_reface was recommended for ADNI‐4 primarily because fewer (zero) images had modified brain (Figure 2‐bottom), and ADNI leadership approved de‐facing with mri_reface. ADNI performed a second validation of its latest version on another diverse sample of 100 ADNI‐3 participants. These analyses included: boundary shift interval at University College London, where average differences in measurements were all <0.05%; cortical white matter hyperintensities at UC Davis, where they found “minimum impact” (R>0.97); and others.ConclusionADNI‐4 de‐faces all indicated image types (Table 1) before subsequent pre‐processing, analyses, and public release. Sites upload images to the central repository (LONI), where they are queued to the MRI Core at Mayo Clinic for de‐facing. Image types requiring de‐facing are de‐faced using mri_reface. Trained analysts inspect de‐faced images to confirm complete removal of the face and complete non‐modification of the brain, and any flagged images are re‐processed manually until issues are resolved. De‐faced images are converted back to DICOM and re‐uploaded for further distribution. Image types not requiring de‐facing are indicated to proceed directly to subsequent steps. In summary, ADNI‐4 has adopted mri_reface because it successfully de‐faces without substantially affecting brain measurements.

AbstractBackgroundRecent advances in automatic face recognition have increased the risk that de‐identified research imaging data could be re‐identified from face imagery in brain scans.MethodAn ADNI committee of independent imaging experts evaluated 11 published techniques for face‐deidentification (“de‐facing”) and selected four algorithms (FSL‐UK Biobank, HCP/XNAT, mri_reface, and BIC) for formal testing using 183 longitudinal scans of 61 racially and ethnically diverse ADNI participants, evaluated by their facial feature removal on 3D rendered surfaces (confirming sufficient privacy protection) and by comparing measurements from ADNI routine image analyses on unmodified vs. de‐faced images (confirming negligible side effects on analyses).ResultEffects of the de‐facing methods on FreeSurfer at UCSF (Figure 1‐top), ASHS at University of Pennsylvania (Figure 1 bottom‐left), and longitudinal atrophy at Mayo Clinic (Figure 1 bottom‐right) were comparable with each other and sufficiently small vs. unmodified scans to recommend de‐facing in ADNI‐4. The FSL and mri_reface methods were more thorough in removing facial features, partly because these also removed ears (Figure 2‐top). mri_reface was recommended for ADNI‐4 primarily because fewer (zero) images had modified brain (Figure 2‐bottom), and ADNI leadership approved de‐facing with mri_reface. ADNI performed a second validation of its latest version on another diverse sample of 100 ADNI‐3 participants. These analyses included: boundary shift interval at University College London, where average differences in measurements were all <0.05%; cortical white matter hyperintensities at UC Davis, where they found “minimum impact” (R>0.97); and others.ConclusionADNI‐4 de‐faces all indicated image types (Table 1) before subsequent pre‐processing, analyses, and public release. Sites upload images to the central repository (LONI), where they are queued to the MRI Core at Mayo Clinic for de‐facing. Image types requiring de‐facing are de‐faced using mri_reface. Trained analysts inspect de‐faced images to confirm complete removal of the face and complete non‐modification of the brain, and any flagged images are re‐processed manually until issues are resolved. De‐faced images are converted back to DICOM and re‐uploaded for further distribution. Image types not requiring de‐facing are indicated to proceed directly to subsequent steps. In summary, ADNI‐4 has adopted mri_reface because it successfully de‐faces without substantially affecting brain measurements.

AbstractINTRODUCTIONRecent technological advances have increased the risk that de‐identified brain images could be re‐identified from face imagery. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a leading source of publicly available de‐identified brain imaging, who quickly acted to protect participants’ privacy.METHODSAn independent expert committee evaluated 11 face‐deidentification (“de‐facing”) methods and selected four for formal testing.RESULTSEffects of de‐facing on brain measurements were comparable across methods and sufficiently small to recommend de‐facing in ADNI. The committee ultimately recommended mri_reface for advantages in reliability, and for some practical considerations. ADNI leadership approved the committee's recommendation, beginning in ADNI4.DISCUSSIONADNI4 de‐faces all applicable brain images before subsequent pre‐processing, analyses, and public release. Trained analysts inspect de‐faced images to confirm complete face removal and complete non‐modification of brain. This paper details the history of the algorithm selection process and extensive validation, then describes the production workflows for de‐facing in ADNI.Highlights ADNI is implementing “de‐facing” of MRI and PET beginning in ADNI4. “De‐facing” alters face imagery in brain images to help protect privacy. Four algorithms were extensively compared for ADNI and mri_reface was chosen. Validation confirms mri_reface is robust and effective for ADNI sequences. Validation confirms mri_reface negligibly affects ADNI brain measurements.

Abstract The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi‐platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1‐weighted and fluid‐attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. Highlights The MRI Core provides multi‐platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups. The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current. As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF‐fMRI; and 2861 HighResHippo (see Table 1 for abbreviations). As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM‐SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF‐fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations). ADNI MRI is an underutilized resource that could be more useful to the research community.

Abstract Although white matter hyperintensities have traditionally been viewed as a marker of vascular disease, recent pathology studies have found an association between white matter hyperintensities and Alzheimer’s disease pathologies. The objectives of this study were to investigate the topographic patterns of white matter hyperintensities associated with Alzheimer’s disease biomarkers measured using PET. From the population-based Mayo Clinic Study of Aging, 434 participants without dementia (55% male) with FLAIR and gradient recall echo MRI, tau-PET (AV-1451) and amyloid-PET scans were identified. A subset had cerebral microbleeds detected on T2* gradient recall echo scans. White matter hyperintensities were semi-automatically segmented using FLAIR MRI in participant space and normalized to a custom template. We used statistical parametric mapping 12-based, voxel-wise, multiple-regression analyses to detect white matter hyperintense regions associated with Alzheimer’s biomarkers (global amyloid from amyloid-PET and meta-regions of interest tau uptake from tau-PET) after adjusting for age, sex and hypertension. For amyloid associations, we additionally adjusted for tau and vice versa. Topographic patterns of amyloid-associated white matter hyperintensities included periventricular white matter hyperintensities (frontal and parietal lobes). White matter hyperintense volumes in the detected topographic pattern correlated strongly with lobar cerebral microbleeds (P < 0.001, age and sex adjusted Cohen’s d = 0.703). In contrast, there were no white matter hyperintense regions significantly associated with increased tau burden using voxel-based analysis or region-specific analysis. Among non-demented elderly, amyloid load correlated with a topographic pattern of white matter hyperintensities. Further, the amyloid-associated, white matter hyperintense regions strongly correlated with lobar cerebral microbleeds suggesting that cerebral amyloid angiopathy contributes to the relationship between amyloid and white matter hyperintensities. The study did not support an association between increased tau burden and white matter hyperintense burden.

Background: Although white matter hyperintensities (WMH) have been viewed as a marker of vascular disease, pathology studies have found an association of WMH with Alzheimer’s disease (AD) pathologies. Our objective was to investigate the topographic patterns of WMH associated with AD biomarkers. Methods: From the population-based Mayo Clinic Study of aging, 434 non-demented participants with FLAIR-MRI, tau-PET and PiB-PET scans were identified. A subset had microbleeds on T2* GRE scans. We ran SPM12 based voxel-wise multiple regression analyses to detect WMH regions associated with AD biomarkers (global amyloid from PiB-PET and meta-ROI tau uptake from tau-PET) adjusting for age, sex, and hypertension. For amyloid associations, we additionally adjusted for tau and vice versa. Results: Topographic patterns of WMH associated with amyloid after both voxel and cluster level corrections primarily included periventricular WMH (frontal and parietal lobes). Masks with lenient threshold (greater detection sensitivity) and strict thresholds (greater detection specificity) are shown in the figure. (A) and (B) show the areas of positive associations between PIB ratio and WMHs adjusted by age, sex, education, hypertension and Tau meta ROI. The sensitive mask (A) was thresholded at FDR p<0.05 at voxel level and FWE p<0.05 at cluster level. The specific mask (B) was thresholded at FDR p<0.001 at voxel level and FDR p<0.05 at cluster level. (C) Shows the frequency of WMHs in the study sample, ranging from infrequent (dark blue) to frequent (green to red). Both the sensitive (p<0.001, Cohen’s D=0.615) and specific WMH maps (p<0.001, Cohen’s D = 0.705) were associated with microbleeds. No WMH regions were associated with tau. Conclusion: Amyloid but not tau load on PET was associated with WMH topographically. The WMH regions associated with amyloid were associated with CMBs suggesting that cerebral amyloid angiopathy may be contributing to the relationship between amyloid and WMH.