Reid, Robert I.

Abstract INTRODUCTION Diffusion‐weighted magnetic resonance imaging (dMRI) is sensitive to the microstructural properties of brain tissues and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest without considering the underlying fiber geometry. METHODS We propose a novel macrostructure‐informed normative tractometry (MINT) framework to investigate how white matter (WM) microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compared MINT‐derived metrics with univariate diffusion tensor imaging (DTI) metrics to examine how fiber geometry may impact the interpretation of microstructure. RESULTS In two multisite cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics' sensitivity to dementia. DISCUSSION We show that MINT, by jointly modeling tract shape and microstructure, has the potential to disentangle and better interpret the effects of degenerative disease on the brain's neural pathways. Highlights Changes in diffusion tensor imaging metrics may be due to macroscopic changes. Normative models encode normal variability of diffusion metrics in healthy controls. Variational autoencoder applied on tractography can learn patterns of fiber geometry. WM microstructure and macrostructure are modeled with multivariate methods. Transfer learning uses pretraining and fine‐tuning for increased efficiency.

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.

AbstractPhase four of the Alzheimer's Disease Neuroimaging Initiative (ADNI4) magnetic resonance imaging (MRI) protocols aim to maintain longitudinal consistency across two decades of data acquisition, while adopting new technologies. Here we describe and justify the study's design and targeted biomarkers. The ADNI4 MRI protocol includes nine MRI sequences. Some sequences require the latest hardware and software system upgrades and are continuously rolled out as they become available at each site. The main sequence additions/changes in ADNI4 are: (1) compressed sensing (CS) T1‐weighting, (2) pseudo‐continuous arterial spin labeling (ASL) on all three vendors (GE, Siemens, Philips), (3) multiple‐post‐labeling‐delay ASL, (4) 1 mm3 isotropic 3D fluid‐attenuated inversion recovery, and (5) CS 3D T2‐weighted. ADNI4 aims to help the neuroimaging community extract valuable imaging biomarkers and provide a database to test the impact of advanced imaging strategies on diagnostic accuracy and disease sensitivity among individuals lying on the cognitively normal to impaired spectrum.Highlights A summary of MRI protocols for phase four of the Alzheimer's Disease Neuroimaging Initiative (ADNI 4). The design and justification for the ADNI 4 MRI protocols. Compressed sensing and multi‐band advances have been applied to improve scan time. ADNI4 protocols aim to streamline safety screening and therapy monitoring. The ADNI4 database will be a valuable test bed for academic research.

AbstractINTRODUCTIONAge‐related and Alzheimer's disease (AD) dementia–related neurodegeneration impact brain health. While morphometric measures from T1‐weighted scans are established biomarkers, they may be less sensitive to earlier changes. Neurite orientation dispersion and density imaging (NODDI), offering biologically meaningful interpretation of tissue microstructure, may be an advanced brain health biomarker.METHODSWe contrasted regional gray matter NODDI and morphometric evaluations concerning their correlation with (1) age, (2) clinical diagnosis stage, and (3) tau pathology as assessed by AV1451 positron emission tomography.RESULTSOur study hypothesizes that NODDI measures are more sensitive to aging and early AD changes than morphometric measures. One NODDI output, free water fraction (FWF), showed higher sensitivity to age‐related changes, generally better effect sizes in separating mild cognitively impaired from cognitively unimpaired participants, and stronger associations with regional tau deposition than morphometric measures.DISCUSSIONThese findings underscore NODDI's utility in capturing early neurodegenerative changes and enhancing our understanding of aging and AD.Highlights Neurite orientation dispersion and density imaging can serve as an effective brain health biomarker for aging and early Alzheimer's disease (AD). Free water fraction has higher sensitivity to normal brain aging. Free water fraction has stronger associations with early AD and regional tau deposition.

Abstract Background White matter (WM) abnormalities have been implicated in clinically relevant functional decline in multiple system atrophy (MSA). Objective To identify the WM and gray matter (GM) abnormalities in MSA and assess the utility of longitudinal structural and diffusion changes as surrogate markers for tracking disease progression in MSA. Methods Twenty‐seven participants with early MSA [15 with clinically predominant cerebellar (MSA‐C) and 12 with clinically predominant parkinsonian features (MSA‐P)] and 14 controls were enrolled as a part of our prospective, longitudinal study of synucleinopathies. Using structural magnetic resonance imaging (MRI) and diffusion MRI (diffusion tensor and neurite orientation and dispersion density imaging), we analyzed whole and regional brain changes in these participants. We also evaluated temporal imaging trajectories based on up to three annual follow‐up scans and assessed the impact of baseline diagnosis on these imaging biomarkers using mixed‐effect models. Results MSA patients exhibited more widespread WM changes than GM, particularly in the cerebellum and brainstem, with greater severity in MSA‐C. Structural and diffusion measures in the cerebellum WM and brainstem deteriorated with disease progression. Rates of progression of these abnormalities were similar in both MSA subtypes, reflecting increasing overlap of clinical features over time. Conclusion WM abnormalities are core features of MSA disease progression and advance at similar rates in clinical MSA subtypes. Multimodal MRI imaging reveals novel insights into the distribution and pattern of brain abnormalities and their progression in MSA. Selected structural and diffusion measures may be useful for tracking disease progression in MSA clinical trials.

AbstractINTRODUCTIONPremenopausal bilateral oophorectomy (PBO) is associated with later‐life cognition, but the underlying brain changes remain unclear. We assessed the impact of PBO and PBO age on white matter integrity.METHODSFemale participants with regional diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA) and mean diffusivity (MD) were included (22 with PBO < 40 years; 43 with PBO 40‐45 years; 39 with PBO 46‐49 years; 907 referents without PBO < 50 years). Linear regression models adjusted for age and apolipoprotein E (APOE) genotype.RESULTSFemales with PBO < 40 years, compared to referents, had lower FA and higher MD in the anterior corona radiata, genu of the corpus collosum, inferior fronto‐occipital fasciculus, superior occipital, and superior temporal white matter. Females who underwent PBO between 45 and 49 also had some changes in white matter integrity.DISCUSSIONFemales who underwent PBO < 40 years had reduced white matter integrity across multiple regions in later‐life. These results are important for females considering PBO for noncancerous conditions.Highlights Females with premenopausal bilateral oophorectomy (PBO) < 40 years had lower FA versus referents. Females with PBO < 40 years had higher MD in many regions versus referents. Adjusting for estrogen replacement therapy use did not attenuate results. Females with PBO 45‐49 years also had some white matter changes versus referents.

Abstract Objective To determine the association between brain MRI abnormalities and incident epilepsy in older adults. Methods Men and women (ages 45–64 years) from the Atherosclerosis Risk in Communities study were followed up from 1987 to 2018 with brain MRI performed between 2011 and 2013. We identified cases of incident late‐onset epilepsy (LOE) with onset of seizures occurring after the acquisition of brain MRI. We evaluated the relative pattern of cortical thickness, subcortical volume, and white matter integrity among participants with incident LOE after MRI in comparison with participants without seizures. We examined the association between MRI abnormalities and incident LOE using Cox proportional hazards regression. Models were adjusted for demographics, hypertension, diabetes, smoking, stroke, and dementia status. Results Among 1251 participants with brain MRI data, 27 (2.2%) developed LOE after MRI over a median of 6.4 years (25–75 percentile 5.8–6.9) of follow‐up. Participants with incident LOE after MRI had higher levels of cortical thinning and white matter microstructural abnormalities before seizure onset compared to those without seizures. In longitudinal analyses, greater number of abnormalities was associated with incident LOE after controlling for demographic factors, risk factors for cardiovascular disease, stroke, and dementia (gray matter: hazard ratio [HR]: 2.3, 95% confidence interval [CI]: 1.0–4.9; white matter diffusivity: HR: 3.0, 95% CI: 1.2–7.3). Interpretation This study demonstrates considerable gray and white matter pathology among individuals with LOE, which is present prior to the onset of seizures and provides important insights into the role of neurodegeneration, both of gray and white matter, and the risk of LOE.

AbstractBackgroundSeveral studies suggest that premenopausal bilateral oophorectomy (PBO) is associated with an increased risk of cognitive impairment and dementia; however, the underlying brain changes are not well understood. Animal studies suggest that long‐term ovarian hormone deprivation in rats is associated with reduced white matter integrity, but few studies have been conducted among women. We assessed the impact of age at PBO before age 50 years on white matter integrity in a community‐based cohort of older women.MethodParticipants included 1,011 women enrolled in the Mayo Clinic Study on Aging (MCSA; 22 with PBO<40 years; 43 with PBO 40‐45 years; 39 with PBO 46‐49 years and 907 referent women). MCSA is a population‐based study of aging in Olmsted County, MN. PBO was abstracted from the medical records. Regional DTI metrics of fractional anisotropy (FA) and mean diffusivity (MD) were investigated using the Johns Hopkins University Atlas. Linear regression was used to examine the association between PBO age group and z‐scores of either FA or MD after adjusting for age at imaging and APOE.ResultThere were no differences in age at study visit (median = 72 years), education, APOE E4 frequency, or vascular comorbidities between the 3 groups of women with a history of PBO or referent women. Women with PBO<40 years, compared to referent women, had lower FA and higher MD in the anterior corona radiata, genu of the corpus collosum, inferior fronto‐occipital fasciculus, superior occipital, and superior temporal white matter. Women with PBO<40 years also had lower inferior frontal white matter FA and higher posterior thalamic radiation MD. Women who underwent PBO between 40‐44 years or 45‐49 generally did not have significant changes in white matter integrity compared to referent women.ConclusionWomen who underwent premenopausal bilateral oophorectomy prior to the age of 40 years had reduced white matter integrity across multiple regions in later‐life. These results are important for the education of women at a low risk for ovarian cancer who are considering PBO for benign conditions.

AbstractBackgroundWe have previously shown that 3D T1‐weighted and 3D T2‐FLAIR brain MRI can potentially be re‐identified using face recognition, but this has not been tested for other MRI sequences commonly used in imaging studies of aging and dementia. Here we compare match rates of automated face recognition to re‐identify 3D T1‐weighted, 3D T2‐FLAIR, 3D T2‐weighted, 2D Axial T2‐FLAIR, multi‐echo GRE (T2*), diffusion MRI, functional MRI, ASL, and T2* GRE images.MethodWe used a dataset of 182 Mayo Clinic participants each with MRI and face photographs. MRIs used Siemens and GE vendor‐product sequences similar to ADNI3 and ADNI4 protocols. We created face reconstructions (“basic”, with minimal preprocessing, and “advanced” that automatically filled missing face regions using a template) from each MRI sequence, and we trained a Microsoft Azure PersonGroup face recognition classifier to recognize these MRI‐based faces. We then queried the classifier with five face photographs for each participant, and we recorded how often the classifier chose the correct MRI as the top‐ranked match. For image types with match rates > = 10%, we then repeated the experiment after de‐facing each MRI with mri_reface, our leading software that replaces faces in each image (“re‐faces”) with an average face.ResultResults are presented in Table 1. 3D T2‐FLAIR, 3D T1‐weighted, and 3D T2‐weighted images were matched at 96‐98%, and mri_reface reduced all to 8%. Axial T2‐FLAIR and ME‐GRE were matched at 32‐45%, and mri_reface reduced both to 1%. T2*‐GRE matched at 10%, and mri_reface reduced this to 1%. ASL matched at 8%, while dMRI and fMRI matched at only 0‐2%.ConclusionAmong current‐generation vendor‐product sequences, we recommend that de‐facing be applied before research data sharing of T1‐weighted, T2‐weighted, T2‐FLAIR, T2*, and ME‐GRE images. Minimal match rates for current‐generation product fMRI, ASL, and fMRI images suggest that they present low risk of re‐identification and can be shared without de‐facing, but this conclusion should be re‐evaluated if they are acquired without fat‐suppression, with a larger field of view, or if newer developments reduce the current levels of artifacts and distortion around the face.