Schwarz, Christopher G.

Abstract Objective To compare iron deposition in the substantia nigra (SN) as measured with quantitative susceptibility mapping (QSM) on antemortem magnetic resonance imaging (MRI) between individuals with and without Lewy‐related pathology at autopsy. Methods We performed a retrospective cohort study including 54 participants who underwent autopsy and antemortem MRI with QSM. Cases were classified as Lewy body disease (LBD)‐present (n = 13) if they had Lewy‐related pathology and LBD‐absent (n = 41) if they did not have Lewy‐related pathology. QSM was calculated for the whole SN and the two subregions: pars compacta (SNc) and pars reticulata (SNr). Nonparametric Wilcoxon rank‐sum tests were used to compare SN QSM values between LBD‐present and LBD‐absent cases. Area under the receiver operating characteristic (ROC) curve (AUC) analyses tested the accuracy of SN QSM values to distinguish the two groups. Associations of QSM values in the SN and its subregions with clinical features were tested with Spearman's correlations. Results The LBD‐present group had higher QSM values in the SNc ( P = 0.008) than the LBD‐absent group with no differences in SNr. QSM values of the SNc distinguished LBD‐present and LBD‐absent cases with good accuracy (AUC = 0.74) and correlated with the presence of parkinsonism and parkinsonism severity. Conclusions This study provides neuropathological confirmation of the utility of SNc QSM as an in vivo biomarker of Lewy‐related pathology. Imaging evidence of abnormal iron deposition in the SNc could potentially serve as a biomarker for inclusion in emerging research frameworks aimed at defining individuals with LBD based on their biological characteristics. Ultimately, this could facilitate more precise diagnoses and guide treatment strategies in LBD. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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.

AbstractBackgroundCorticobasal syndrome (CBS) is a progressive neurodegenerative syndrome that can result from several underlying neuropathologies, including Alzheimer’s disease (AD). Little is known about the utility of blood plasma measures to predict PET biomarker confirmed AD in CBS.MethodEighteen CBS patients (8 PET Aß+; 10 PET Aß‐) and 8 cognitively unimpaired Aß‐ individuals (CU) were recruited by the Neurodegenerative Research group from the Department of Neurology, Mayo Clinic, Rochester, MN, underwent Aß (Pittsburgh Compound‐B) and tau (18F‐flortaucipir) PET and provided a blood sample. Blood plasma analysis was performed to assess p‐tau 181, total tau, Aß 42/40, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) using the Quanterix Simoa platform. Plasma concentrations were compared between Aß‐, and Aß+ CBS and controls using Kruskal‐Wallis corrected for multiple comparisons using Dunn’s test, with effect size measured with area under the receiver operating characteristic curve (AUROC) analysis. Spearman correlations were calculated to assess relationships between plasma concentrations and Aß and tau PET uptake.ResultThe CBS Aß+ group showed reduced Aß42/40 ratio, with elevated p‐tau 181, GFAP and NfL plasma concentrations compared to CU, while the CBS Aß‐ group only showed elevated NfL concentration compared to CU (Figure 1). Both p‐tau 181 (AUROC=0.95) and GFAP (AUROC=0.91) plasma concentrations were able to differentiate CBS Aß‐ from CBS Aß+. Plasma p‐tau 181 and GFAP concentrations showed a positive correlation to both Aß and tau PET uptake, and the Aß42/40 ratio showed a negative correlation to Aß PET uptake (Figure 2). No correlations were observed between NfL and Aß and tau PET.ConclusionThis study supports the use of plasma p‐tau 181 and GFAP to detect AD in CBS, while NfL shows potential as a nonspecific disease biomarker of CBS regardless of underlying pathology. Plasma concentrations of these biomarkers may potentially be incorporated into clinical trials targeting CBS patients with underlying AD pathology.

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.

Abstract INTRODUCTION Premenopausal bilateral oophorectomy (PBO) before the age of 46 years is associated with an increased risk of dementia. We investigated the long‐term effects of PBO performed before age 50 years on amyloid beta (Aβ), tau, and neurodegeneration imaging biomarkers of Alzheimer's disease (AD). METHODS Mayo Clinic Cohort Study of Oophorectomy and Aging‐2 participants were divided into early PBO (< 46 years; n = 61), and late PBO (46–49 years; n = 51) groups and were compared to referent women who did not undergo PBO ( n = 119). RESULTS Early PBO was associated with thinner entorhinal cortex ( p = 0.014), higher tau load at higher levels of Aβ load ( Pp = 0.005), higher Aβ load ( p = 0.026), and smaller temporal lobe cortical thickness ( p = 0.022), only at older ages compared to the referent group. DISCUSSION PBO before the age of 46 years is associated with entorhinal cortex thinning, elevated tau at higher Aβ levels, along with an AD‐like pattern of atrophy at older ages. CLINICAL TRIALS REGISTRATION NCT03821857 sex‐specific effects of endocrine disruption on aging and AD. Highlights Premenopausal bilateral oophorectomy (PBO) before the ages of 46 (early PBO) years and ages 46 to 49 (late PBO) years was studied. Early PBO was associated with reduced entorhinal cortex thickness later in life. Early PBO was associated with greater amyloid beta (Aβ) load at older ages. Early PBO was associated with greater Alzheimer's disease pattern of atrophy at older ages. Early PBO was associated with higher tau load at higher Aβ levels.

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.