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Background: Limited information exists regarding the association between midlife lipid levels and late-life total and regional brain volumes. Methods: We studied 1872 participants in the longitudinal community-based Atherosclerosis Risk in Communities Neurocognitive Study. Serum lipid levels were measured in 1987–1989 (mean age, 53 ± 5 years). Participants underwent 3T brain MRI scans in 2011–2013. Brain volumes were measured using FreeSurfer image analysis software. Linear regression models were used to assess the associations between serum lipids and brain volumes modeled in standard deviation (SD) units, adjusting for potential confounders. Results: In adjusted analyses, one SD higher low-density lipoprotein cholesterol (LDL) levels were associated with larger total brain volumes (β 0.033, 95% CI 0.006–0.060) as well as larger volumes of the temporal (β 0.038, 95% CI 0.003–0.074) and parietal lobes (β 0.044, 95% CI 0.009–0.07) and Alzheimer disease-related region (β 0.048, 95% CI 0.048–0.085). Higher triglyceride levels were associated with smaller total brain volumes (β -0.033, 95% CI -0.060, -0.007). The associations between LDL levels and brain volumes were modified by age (P for interaction 53 years at baseline, and were attenuated after application of weights to account for informative attrition, although associations with the parietal and Alzheimer's disease-related region remained significant. High-density lipoprotein cholesterol was not associated with brain volumes. Conclusion: Higher LDL levels in late midlife were associated with larger brain volumes later in life, while higher triglyceride levels were associated with smaller brain volumes. These associations were driven by adults >53 years at baseline.

Abstract INTRODUCTION Age‐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. METHODS We 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. RESULTS Our 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. DISCUSSION These 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.

Posterior cortical atrophy (PCA) and dementia with Lewy bodies (DLB) show distinct atrophy and overlapping hypometabolism profiles, but it is unknown how disruptions in structural and functional connectivity compare between these disorders and whether breakdowns in connectivity relate to either atrophy or hypometabolism.Thirty amyloid-positive PCA patients, 24 amyloid-negative DLB patients and 30 amyloid-negative cognitively unimpaired (CU) healthy individuals were recruited at Mayo Clinic, Rochester, MN, and underwent a 3T head MRI, including structural MRI, resting state functional MRI (rsfMRI) and diffusion tensor imaging (DTI) sequences, as well as [18F] fluorodeoxyglucose (FDG) PET. We assessed functional connectivity within and between 12 brain networks using rsfMRI and the CONN functional connectivity toolbox and calculated regional DTI metrics using the Johns Hopkins atlas. Multivariate linear-regression models corrected for multiple comparisons and adjusted for age and sex compared DTI metrics and within-network and between-network functional connectivity across groups. Regional gray-matter volumes and FDG-PET standard uptake value ratios (SUVRs) were calculated and analyzed at the voxel-level using SPM12. We used univariate linear-regression models to investigate the relationship between connectivity measures, gray-matter volume, and FDG-PET SUVR.On DTI, PCA showed degeneration in occipito-parietal white matter, posterior thalamic radiations, splenium of the corpus collosum and sagittal stratum compared to DLB and CU, with greater degeneration in the temporal white matter and the fornix compared to CU. We observed no white-matter degeneration in DLB compared to CU. On rsfMRI, reduced within-network connectivity was present in dorsal and ventral default mode networks (DMN) and the dorsal-attention network in PCA compared to DLB and CU, with reduced within-network connectivity in the visual and sensorimotor networks compared to CU. DLB showed reduced connectivity in the cerebellar network compared to CU. Between-network analysis showed increased connectivity in both cerebellar-to-sensorimotor and cerebellar-to-dorsal attention network connectivity in PCA and DLB. PCA showed reduced anterior DMN-to-cerebellar and dorsal attention-to-sensorimotor connectivity, while DLB showed reduced posterior DMN-to-sensorimotor connectivity compared to CU. PCA showed reduced dorsal DMN-to-visual connectivity compared to DLB. The multimodal analysis revealed weak associations between functional connectivity and volume in PCA, and between functional connectivity and metabolism in DLB.These findings suggest that PCA and DLB have unique connectivity alterations, with PCA showing more widespread disruptions in both structural and functional connectivity; yet some overlap was observed with both disorders showing increased connectivity from the cerebellum.

Primary progressive aphasia (PPA) variants present with distinct disruptions in speech-language functions with little known about the interplay between affected and spared regions within the speech-language network and their interaction with other functional networks.The Neurodegenerative Research Group, Mayo Clinic, recruited 123 patients with PPA (55 logopenic (lvPPA), 44 non-fluent (nfvPPA) and 24 semantic (svPPA)) who were matched to 60 healthy controls. We investigated functional connectivity disruptions between regions within the left-speech-language network (Broca, Wernicke, anterior middle temporal gyrus (aMTG), supplementary motor area (SMA), planum temporale (PT) and parietal operculum (PO)), and disruptions to other networks (visual association, dorsal-attention, frontoparietal and default mode networks (DMN)).Within the speech-language network, multivariate linear regression models showed reduced aMTG-Broca connectivity in all variants, with lvPPA and nfvPPA findings remaining significant after Bonferroni correction. Additional loss in Wernicke-Broca connectivity in nfvPPA, Wernicke-PT connectivity in lvPPA and greater aMTG-PT connectivity in svPPA were also noted. Between-network connectivity findings in all variants showed reduced aMTG-DMN and increased aMTG-dorsal-attention connectivity, with additional disruptions between aMTG-visual association in both lvPPA and svPPA, aMTG-frontoparietal in lvPPA, and Wernicke-DMN breakdown in svPPA.These findings suggest that aMTG connectivity breakdown is a shared feature in all PPA variants, with lvPPA showing more extensive connectivity disruptions with other networks.

Introduction: AD and CVD, which frequently co-occur, are leading causes of age-related cognitive decline. We assessed how demographic factors, socioeconomic status (SES) as indicated by education and occupation, vascular risk factors, and a range of biomarkers associated with both CVD (including white matter hyperintensities [WMH], diffusion MRI abnormalities, infarctions, and microbleeds) and AD (comprising amyloid-PET and tau-PET) collectively influence cognitive function. Methods: In this cross-sectional population study, structural equation models were utilized to understand these associations in 449 participants (mean age (SD) = 74.5 (8.4) years; 56% male; 7.5% cognitively impaired). Results: (1) Higher SES had a protective effect on cognition with mediation through the vascular pathway. (2) The effect of amyloid directly on cognition and through tau was 11-fold larger than the indirect effect of amyloid on cognition through WMH. (3) There is a significant effect of vascular risk on tau deposition. Discussion: The utilized biomarkers captured the impact of CVD and AD on cognition. The overall effect of vascular risk and SES on these biomarkers are complex and need further investigation.

Abstract INTRODUCTION This study evaluated the performance of the Lumipulse plasma beta‐amyloid (Aβ) 42/40 and pTau181 compared to other assays to detect an abnormal amyloid‐positron emission tomography (PET). METHODS Plasma samples from cognitively unimpaired (N = 179) and MCI/AD dementia (N = 36) individuals were retrospectively evaluated. Plasma Aβ42/40 and pTau181 were measured using the Lumipulse and Simoa immunoassays. An immunoprecipitation mass spectrometry (IP‐MS) assay for plasma Aβ42/40 was also evaluated. Amyloid‐PET status was the outcome measure. RESULTS Lumipulse and IP‐MS Aβ42/40 exhibited the highest diagnostic accuracy for detecting an abnormal amyloid‐PET (areas under the curve [AUCs] of 0.81 and 0.84, respectively). The Lumipulse and Simoa pTau181 assays exhibited lower performance (AUCs of 0.74 and 0.72, respectively). The Simoa Aβ42/40 assay demonstrated the lowest diagnostic accuracy (AUC 0.57). Combining Aβ42/40 and pTau181 did not significantly improve performance over Aβ42/40 alone for Lumipulse (AUC 0.83) or over pTau181 alone for Simoa (AUC 0.71) DISCUSSION The Lumipulse Aβ42/40 assay showed similar performance to the IP‐MS Aβ42/40 assay for detection of an abnormal amyloid‐PET; and both assays performed better than the two p‐tau181 immunoassays. The Simoa Aβ42/Aβ40 assay was the least accurate at predicting an abnormal amyloid‐PET status. Highlights Lumipulse plasma Aβ42/Aβ40 AUC for abnormal amyloid‐PET detection was 0.81. This performance was comparable to previously reported IP‐MS and higher than Simoa. Performance of Alzheimer's disease blood biomarkers varies between assays.

Objective To examine interactions between Neuropsychiatric symptoms (NPS) with Pittsburgh Compound B (PiB) and fluorodeoxyglucose positron emission tomography (FDG‐PET) in predicting cognitive trajectories. Methods We conducted a longitudinal study in the setting of the population‐based Mayo Clinic Study of Aging in Olmsted County, MN, involving 1581 cognitively unimpaired (CU) persons aged ≥50 years (median age 71.83 years, 54.0% males, 27.5% APOE ɛ4 carriers). NPS at baseline were assessed using the Neuropsychiatric Inventory Questionnaire (NPI‐Q). Brain glucose hypometabolism was defined as a SUVR ≤ 1.47 (measured by FDG‐PET) in regions typically affected in Alzheimer's disease. Abnormal cortical amyloid deposition was measured using PiB‐PET (SUVR ≥ 1.48). Neuropsychological testing was done approximately every 15 months, and we calculated global and domain‐specific (memory, language, attention, and visuospatial skills) cognitive z‐scores. We ran linear mixed‐effect models to examine the associations and interactions between NPS at baseline and z‐scored PiB‐ and FDG‐PET SUVRs in predicting cognitive z‐scores adjusted for age, sex, education, and previous cognitive testing. Results Individuals at the average PiB and without NPS at baseline declined over time on cognitive z‐scores. Those with increased PiB at baseline declined faster (two‐way interaction), and those with increased PiB and NPS declined even faster (three‐way interaction). We observed interactions between time, increased PiB and anxiety or irritability indicating accelerated decline on global z‐scores, and between time, increased PiB and several NPS (e.g., agitation) showing faster domain‐specific decline, especially on the attention domain. Conclusions NPS and increased brain amyloid deposition synergistically interact in accelerating global and domain‐specific cognitive decline among CU persons at baseline.

A sensitive and accurate imaging technique capable of tracking the disease progression of Alzheimer's Disease (AD) driven amnestic dementia would be beneficial. A currently available method for pathology detection in AD with high accuracy is Positron Emission Tomography (PET) imaging, despite certain limitations such as low spatial resolution, off-targeting error, and radiation exposure. Non-invasive MRI scanning with quantitative magnetic susceptibility measurements can be used as a complementary tool. To date, quantitative susceptibility mapping (QSM) has widely been used in tracking deep gray matter iron accumulation in AD. The present work proposes that by compartmentalizing quantitative susceptibility into paramagnetic and diamagnetic components, more holistic information about AD pathogenesis can be acquired. Particularly, diamagnetic component susceptibility (DCS) can be a powerful indicator for tracking protein accumulation in the gray matter (GM), demyelination in the white matter (WM), and relevant changes in the cerebrospinal fluid (CSF). In the current work, voxel-wise group analysis of the WM and the CSF regions show significantly lower |DCS| (the absolute value of DCS) value for amnestic dementia patients compared to healthy controls. Additionally, |DCS| and τ PET standardized uptake value ratio (SUVr) were found to be associated in several GM regions typically affected by τ deposition in AD. Therefore, we propose that the separated diamagnetic susceptibility can be used to track pathological neurodegeneration in different tissue types and regions of the brain. With the initial evidence, we believe the usage of compartmentalized susceptibility demonstrates substantive potential as an MRI-based technique for tracking AD-driven neurodegeneration.

Abstract The aim of this cross‐sectional study was to examine whether a history of selective estrogen receptor modifiers (SERMs), tamoxifen and raloxifene, use was associated with cognitive performance, odds of mild cognitive impairment (MCI), or magnetic resonance imaging (MRI) markers of neurodegeneration associated with Alzheimer's disease. We included women with prior history of breast cancer or no prior history of any cancer at enrollment in the Mayo Clinic Study of Aging (MCSA). This information was abstracted using the Rochester Epidemiology Project medical‐linkage system. Logistic regression was used to examine associations of SERMs with odds of MCI. Linear regression models were used to examine associations of SERMs with cognitive z‐scores (Memory, Executive Function, Language, Visuospatial Skills, Global Cognition), and MRI markers. Among 2840 women aged 50 and older in the MCSA, 151 had a history of breast cancer, and 42 (28%) of these had a history of tamoxifen treatment. A total of 2235 women had no prior history of any cancer, and 76 (3%) of these had a history of raloxifene use. No significant associations between tamoxifen use and cognition, or odds of MCI were observed among women with a history of breast cancer after adjusting for confounders. Similarly, raloxifene use was not significantly associated with cognition, or odds of MCI in women without a history of cancer after adjusting for confounders. We did not find significant associations between the use of either SERM and MRI markers. Use of tamoxifen or raloxifene was not significantly associated with cognition in postmenopausal women.

It is now widely known that research brain MRI, CT, and PET images may potentially be re-identified using face recognition, and this potential can be reduced by applying face-deidentification (“de-facing”) software. However, for research MRI sequences beyond T1-weighted (T1-w) and T2-FLAIR structural images, the potential for re-identification and quantitative effects of de-facing are both unknown, and the effects of de-facing T2-FLAIR are also unknown. In this work we examine these questions (where applicable) for T1-w, T2-w, T2*-w, T2-FLAIR, diffusion MRI (dMRI), functional MRI (fMRI), and arterial spin labelling (ASL) sequences. Among current-generation, vendor-product research-grade sequences, we found that 3D T1-w, T2-w, and T2-FLAIR were highly re-identifiable (96–98%). 2D T2-FLAIR and 3D multi-echo GRE (ME-GRE) were also moderately re-identifiable (44–45%), and our derived T2* from ME-GRE (comparable to a typical 2D T2*) matched at only 10%. Finally, diffusion, functional and ASL images were each minimally re-identifiable (0–8%). Applying de-facing with mri_reface version 0.3 reduced successful re-identification to ≤8%, while differential effects on popular quantitative pipelines for cortical volumes and thickness, white matter hyperintensities (WMH), and quantitative susceptibility mapping (QSM) measurements were all either comparable with or smaller than scan-rescan estimates. Consequently, high-quality de-facing software can greatly reduce the risk of re-identification for identifiable MRI sequences with only negligible effects on automated intracranial measurements. The current-generation echo-planar and spiral sequences (dMRI, fMRI, and ASL) each had minimal match rates, suggesting that they have a 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 full-face scan coverage, or if newer developments reduce the current levels of artifacts and distortion around the face.

Abstract INTRODUCTION We examined associations between plasma‐derived biomarkers of Alzheimer's disease (AD) and neuropsychiatric symptoms (NPS) in community‐dwelling older adults. METHODS Cross‐sectional study involving 1005 persons ≥50 years of age (mean 74 years, 564 male, 118 cognitively impaired), who completed plasma‐derived biomarker (amyloid beta 42 [Aβ42]/Aβ40, phosphorylated tau 181 [p‐tau181], p‐tau217, total tau [t‐tau], neurofilament light [NfL]), and NPS assessment. RESULTS P‐tau181 (odds ratio [OR] 2.06, 95% confidence interval [CI] 1.41–3.00, p

The clinical usefulness MRI biomarkers for aging and dementia studies relies on precise brain morphological measurements; however, scanner and/or protocol variations may introduce noise or bias. One approach to address this is post-acquisition scan harmonization. In this work, we evaluate deep learning (neural style transfer, CycleGAN and CGAN), histogram matching, and statistical (ComBat and LongComBat) methods. Participants who had been scanned on both GE and Siemens scanners (cross-sectional participants, known as Crossover (n = 113), and longitudinally scanned participants on both scanners (n = 454)) were used. The goal was to match GE MPRAGE (T1-weighted) scans to Siemens improved resolution MPRAGE scans. Harmonization was performed on raw native and preprocessed (resampled, affine transformed to template space) scans. Cortical thicknesses were measured using FreeSurfer (v.7.1.1). Distributions were checked using Kolmogorov-Smirnov tests. Intra-class correlation (ICC) was used to assess the degree of agreement in the Crossover datasets and annualized percent change in cortical thickness was calculated to evaluate the Longitudinal datasets. Prior to harmonization, the least agreement was found at the frontal pole (ICC = 0.72) for the raw native scans, and at caudal anterior cingulate (0.76) and frontal pole (0.54) for the preprocessed scans. Harmonization with NST, CycleGAN, and HM improved the ICCs of the preprocessed scans at the caudal anterior cingulate (>0.81) and frontal poles (>0.67). In the Longitudinal raw native scans, over- and under-estimations of cortical thickness were observed due to the changing of the scanners. ComBat matched the cortical thickness distributions throughout but was not able to increase the ICCs or remove the effects of scanner changeover in the Longitudinal datasets. CycleGAN and NST performed slightly better to address the cortical thickness variations between scanner change. However, none of the methods succeeded in harmonizing the Longitudinal dataset. CGAN was the worst performer for both datasets. In conclusion, the performance of the methods was overall similar and region dependent. Future research is needed to improve the existing approaches since none of them outperformed each other in terms of harmonizing the datasets at all ROIs. The findings of this study establish framework for future research into the scan harmonization problem.

Brain imaging research studies increasingly use “de-facing” software to remove or replace facial imagery before public data sharing. Several works have studied the effects of de-facing software on brain imaging biomarkers by directly comparing automated measurements from unmodified vs de-faced images, but most research brain images are used in analyses of correlations with cognitive measurements or clinical statuses, and the effects of de-facing on these types of imaging-to-cognition correlations has not been measured. In this work, we focused on brain imaging measures of amyloid (A), tau (T), neurodegeneration (N), and vascular (V) measures used in Alzheimer’s Disease (AD) research. We created a retrospective sample of participants from three age- and sex-matched clinical groups (cognitively unimpaired, mild cognitive impairment, and AD dementia, and we performed region- and voxel-wise analyses of: hippocampal volume (N), white matter hyperintensity volume (V), amyloid PET (A), and tau PET (T) measures, each from multiple software pipelines, on their ability to separate cognitively defined groups and their degrees of correlation with age and Clinical Dementia Rating (CDR)–Sum of Boxes (CDR-SB). We performed each of these analyses twice: once with unmodified images and once with images de-faced with leading de-facing software mri_reface, and we directly compared the findings and their statistical strengths between the original vs. the de-faced images. Analyses with original and with de-faced images had very high agreement. There were no significant differences between any voxel-wise comparisons. Among region-wise comparisons, only three out of 55 correlations were significantly different between original and de-faced images, and these were not significant after correction for multiple comparisons. Overall, the statistical power of the imaging data for AD biomarkers was almost identical between unmodified and de-faced images, and their analyses results were extremely consistent.

Purpose: Progressive apraxia of speech (PAOS) is a neurodegenerative disorder affecting the planning or programming of speech. Little is known about its magnetic susceptibility profiles indicative of biological processes such as iron deposition and demyelination. This study aims to clarify (1) the pattern of susceptibility in PAOS patients, (2) the susceptibility differences between the phonetic (characterized by predominance of distorted sound substitutions and additions) and prosodic (characterized by predominance of slow speech rate and segmentation) subtypes of PAOS, and (3) the relationships between susceptibility and symptom severity. Methods: Twenty patients with PAOS (nine phonetic and eleven prosodic subtypes) were prospectively recruited and underwent a 3 Tesla MRI scan. They also underwent detailed speech, language, and neurological evaluations. Quantitative susceptibility maps (QSM) were reconstructed from multi-echo gradient echo MRI images. Region of interest analysis was conducted to estimate susceptibility coefficients in several subcortical and frontal regions. We compared susceptibility values between PAOS and an age-matched control group and performed a correlation analysis between susceptibilities and an apraxia of speech rating scale (ASRS) phonetic and prosodic feature ratings. Results: The magnetic susceptibility of PAOS was statistically greater than that of controls in subcortical regions (left putamen, left red nucleus, and right dentate nucleus) (p

Background: Aging and dementia involve the disruption of brain molecular pathways leading to the alterations in tissue composition and gross morphology of the brain. Phenotypic and biomarker overlap between various etiologies of dementia supports a need for new modes of information to more accurately distinguish these disorders. Brain mechanical properties, which can be measured noninvasively by MR elastography, represent one understudied feature that are sensitive to neurodegenerative processes. In this study, we used two stiffness estimation schemes to test the hypothesis that different etiologies of dementia are associated with unique patterns of mechanical alterations across the cerebral cortex. Methods: MR elastography data were acquired for six clinical groups including amyloid-negative cognitively unimpaired (CU), amyloid-positive cognitively unimpaired (A + CU), amyloid-positive participants with mild cognitive impairment (A + MCI), amyloid-positive participants with Alzheimer’s clinical syndrome (A + ACS), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD). Stiffness maps were computed using two neural network inversions with the objective to at least partially separate the parenchyma-specific and morphological effects of neurodegeneration on mechanical property estimates. A tissue-confined inversion algorithm was designed to obtain the best estimate of stiffness in the brain parenchyma itself, while a regionally-aware inversion algorithm was used to measure the tissue stiffness along with the surroundings. Mean stiffness of 15 bilateral gray matter cortical regions were considered for statistical analysis. First, we tested the hypothesis that cortical stiffness changes in the aging brain. Next, we tested the overall study hypothesis by first comparing stiffness in each clinical group to the CU group, and then comparing the clinical groups against one another. Finally, we assessed the spatial and statistical overlap between atrophy and stiffness changes for both inversions. Results: Cortical brain regions become softer with age for both inversions with larger effects observed using regionally-aware stiffness. Stiffness decreases in the range 0.010–0.027 kPa per year were observed. Pairwise comparisons of each clinical group with cognitively unimpaired participants demonstrated 5 statistically significant differences in stiffness for tissue-confined measurements and 19 statistically different stiffness changes for the regionally-aware stiffness measurements. Pairwise comparisons between clinical groups further demonstrated unique patterns of stiffness differences. Analysis of the atrophy-versus-stiffness relationship showed that regionally-aware stiffness measurements exhibit higher sensitivity to neurodegeneration with findings that are not fully explained by partial volume effects or atrophy. Conclusions: Both tissue-confined and regionally-aware stiffness estimates exhibited unique and complementary stiffness differences in various etiologies of dementia. Our results suggest that mechanical alterations measured by MRE reflect both tissue-specific differences as well as environmental effects. Multi-inversion schemes in MRE may provide new insights into the relationships between neuropathology and brain biomechanics.

Specific brain structures (gray matter regions and white matter tracts) play a dominant role in determining cognitive decline and explain the heterogeneity in cognitive aging. Identification of these structures is crucial for screening of older adults at risk of cognitive decline. Using deep learning models augmented with a model-interpretation technique on data from 1432 Mayo Clinic Study of Aging participants, we identified a subset of brain structures that were most predictive of individualized cognitive trajectories and indicative of cognitively resilient vs. vulnerable individuals. Specifically, these structures explained why some participants were resilient to the deleterious effects of elevated brain amyloid and poor vascular health. Of these, medial temporal lobe and fornix, reflective of age and pathology-related degeneration, and corpus callosum, reflective of inter-hemispheric disconnection, accounted for 60% of the heterogeneity explained by the most predictive structures. Our results are valuable for identifying cognitively vulnerable individuals and for developing interventions for cognitive decline.

Abstract Objective To assess the relationships between MRI volumetry and [18F]flortaucipir PET of typical and atypical clinical phenotypes of Alzheimer’s disease, by genarian (age by decade). Methods Five‐hundred and sixty‐four participants including those with typical (n = 86) or atypical (n = 80) Alzheimer’s dementia and normal controls (n = 398) underwent apolipoprotein E genotyping, MRI, flortaucipir, and 11C‐PiB; all 166 Alzheimer’s participants were beta‐amyloid positive and all controls were beta‐amyloid negative. Grey matter volume and flortaucipir standard uptake value ratios were calculated for hippocampus, entorhinal cortex, and neocortex. Ratios of hippocampal‐to‐neocortical and entorhinal‐to‐neocortical volume and flortaucipir uptake were also calculated. Linear regression models assessed relationships among regional volume, flortaucipir uptake, and ratios and phenotypes, within three genarians (50–59, 60–69, and 70+). Voxel‐level analyses were also performed. Results For 50–59 greater medial temporal atrophy and flortaucipir uptake was observed in the typical compared with atypical phenotype. The typical phenotype also showed greater frontal neocortex uptake with the voxel‐level analysis. For 60–69 and 70+ there was greater hippocampal volume loss in the typical compared with atypical phenotype while only the 60–69, but not the 70+ group, showed a difference in hippocampal flortaucipir uptake. We also observed a pattern for higher neocortical flortaucipir uptake to correlate with younger age decade for both phenotypes. Interpretation MRI volumetry versus flortaucipir PET relationships differ across Alzheimer’s clinical phenotypes, and also within phenotype across age decades. This suggests that there is potential risk of masked effects by not accounting for genarian in participants with beta‐amyloid and tau‐positive biomarker defined Alzheimer’s disease.

Abstract Objective To examine the association of baseline elevated brain amyloid and neurodegeneration with changes in activities of daily living in participants without dementia (ND; i.e., cognitively unimpaired and participants with mild cognitive impairment) at baseline in the population‐based Mayo Clinic Study of Aging. Methods We included 1747 ND participants with 11C‐PiB PET and MR imaging in the study, with data on activities of daily living (as assessed by the Functional Activities Questionnaire (FAQ) and the Clinical Dementia Rating scale Sum of Boxes for functional domains (CDR‐SOB (functional)), with a median (range) of 4.3 (0.0–12.7) years of follow‐up. Abnormal (elevated; A+) 11C‐PiB‐PET retention ratio was defined as standardized uptake value ratio ≥ 1.48, and abnormal (reduced) AD signature cortical thickness as ≤ 2.68 mm (neurodegeneration; N+). Associations were examined with mixed effects models, adjusting for age, sex, education, apolipoprotein E ε4 allele carrier status, and global cognitive z‐score. Results Mean age (SD) was 71.4 years (10.1), 46.7% were females, 195 (11.2%) had A+N‐, 442 (25.3%) had A‐N+, and 339 (19.4%) had A+N+ biomarkers. The A+N+ group had the largest annualized change in the FAQ score from baseline (difference in annual change A+N+ vs. A‐N‐; ß (SE): 0.80 (0.07)); associations were substantially attenuated when a time‐varying global cognitive composite was included in the model (A+N+ vs. A‐N‐; ß (SE): 0.31 (0.05)). CDR‐SOB (functional) findings partly agreed with FAQ score findings. Interpretation The longitudinal increase in functional limitations is greater for individuals with abnormal neuroimaging biomarkers, especially for those with both elevated brain amyloid and neurodegeneration.

Our goal was to understand the complex relationship between age, sex, midlife risk factors, and early white matter changes measured by diffusion tensor imaging (DTI) and their role in the evolution of longitudinal white matter hyperintensities (WMH). We identified 1564 participants (1396 cognitively unimpaired, 151 mild cognitive impairment and 17 dementia participants) with age ranges of 30–90 years from the population-based sample of Mayo Clinic Study of Aging. We used computational causal structure discovery and regression analyses to evaluate the predictors of WMH and DTI, and to ascertain the mediating effect of DTI on WMH. We further derived causal graphs to understand the complex interrelationships between midlife protective factors, vascular risk factors, diffusion changes, and WMH. Older age, female sex, and hypertension were associated with higher baseline and progression of WMH as well as DTI measures (P ≤ 0.003). The effects of hypertension and sex on WMH were partially mediated by microstructural changes measured on DTI. Higher midlife physical activity was predictive of lower WMH through a direct impact on better white matter tract integrity as well as an indirect effect through reducing the risk of hypertension by lowering BMI. This study identified key risks factors, early brain changes, and pathways that may lead to the evolution of WMH.

Transactive response DNA-binding protein of ∼43 kDa (TDP-43), a primary pathologic substrate in tau-negative frontotemporal lobar degeneration (FTLD), is also often found in the brains of elderly individuals without FTLD and is a key player in the process of neurodegeneration in brains with and without FTLD. It is unknown how rates and trajectories of TDP-43-associated brain atrophy compare between these two groups. Additionally, non-FTLD TDP-43 inclusions are not homogeneous and can be divided into two morphologic types: type-α and neurofibrillary tangle-associated type-β. Therefore, we explored whether neurodegeneration also varies due to the morphologic type. In this longitudinal retrospective study of 293 patients with 843 MRI scans spanning over ∼10 years, we used a Bayesian hierarchical linear model to quantify similarities and differences between the non-FTLD TDP-43 (type-α/type-β) and FTLD-TDP (n = 68) in both regional volume at various timepoints before death and annualized rate of atrophy. Since Alzheimer’s disease (AD) is a frequent co-pathology in non-FTLD TDP-43, we further divided types α/β based on presence/absence of intermediate-high likelihood AD: AD-TDP type-β (n = 90), AD-TDP type-α (n = 104), and Pure-TDP (n = 31, all type-α). FTLD-TDP was associated with faster atrophy rates in the inferior temporal lobe and temporal pole compared to all non-FTLD TDP-43 groups. The atrophy rate in the frontal lobe was modulated by age with younger FTLD-TDP having the fastest rates. Older FTLD-TDP showed a limbic predominant pattern of neurodegeneration. AD-TDP type-α showed faster rates of hippocampal atrophy and smaller volumes of amygdala, temporal pole, and inferior temporal lobe compared to AD-TDP type-β. Pure-TDP was associated with slowest rates and less atrophy in all brain regions. The results suggest that there are differences and similarities in longitudinal brain volume loss between FTLD-TDP and non-FTLD TDP-43. Within FTLD-TDP age plays a role in which brain regions are the most affected. Additionally, brain atrophy regional rates also vary by non-FTLD TDP-43 type.

Abstract Introduction Practical algorithms predicting the probability of amyloid pathology among patients with subjective cognitive decline or mild cognitive impairment may help clinical decisions regarding confirmatory biomarker testing for Alzheimer's disease. Methods Algorithm feature selection was conducted with Alzheimer's Disease Neuroimaging Initiative and Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing data. Probability algorithms were developed in Alzheimer's Disease Neuroimaging Initiative using nested cross‐validation accompanied by stratified subsampling to obtain 1000 internally validated decision trees. Semi‐independent validation was conducted using Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing. Independent external validation was conducted in the population‐based Mayo Clinic Study of Aging. Results Two algorithms were developed using age and normalized immediate recall z‐scores, with or without apolipoprotein E ε4 carrier status. Both algorithms had robust performance across data sets and when substituting different recall memory tests. Discussion The statistical framework resulted in robust probability estimation. Application of these algorithms may assist in clinical decision‐making for further testing to diagnose amyloid pathology.

Using positron emission tomography (PET)-derived amyloid and tau measurements from 1,495 participants, we explore the evolution of these values over time via an accelerated failure time (AFT) model. The AFT model assumes a shared pattern of progression, but one which is shifted earlier or later in time for each individual; an individual's time shift for amyloid and for tau are assumed to be linked. The resulting pattern for each outcome consists of an earlier indolent phase followed by sharp progression of the accumulation rate. APOE ε4 shifts the amyloid curve leftward (earlier) by 6.1 years, and the tau curve leftward by 2.6 years. Female sex shifts the amyloid curve leftward by 2.4 years and the tau curve leftward by 2.6 years. Per-person shifts (i.e., the individual's deviation from the population mean) for the onset of amyloid accumulation ranged from 13 years earlier to 13 years later (10th to 90th percentile) than average and 11 years earlier to 14 years later for tau, with an estimated correlation of 0.49. The average delay between amyloid increase and tau increase was 13.3 years.

Since tau PET tracers were introduced, investigators have quantified them using a wide variety of automated methods. As longitudinal cohort studies acquire second and third time points of serial within-person tau PET data, determining the best pipeline to measure change has become crucial. We compared a total of 415 different quantification methods (each a combination of multiple options) according to their effects on a) differences in annual SUVR change between clinical groups, and b) longitudinal measurement repeatability as measured by the error term from a linear mixed-effects model. Our comparisons used MRI and Flortaucipir scans of 97 Mayo Clinic study participants who clinically either: a) were cognitively unimpaired, or b) had cognitive impairments that were consistent with Alzheimer's disease pathology. Tested methods included cross-sectional and longitudinal variants of two overarching pipelines (FreeSurfer 6.0, and an in-house pipeline based on SPM12), three choices of target region (entorhinal, inferior temporal, and a temporal lobe meta-ROI), five types of partial volume correction (PVC) (none, two-compartment, three-compartment, geometric transfer matrix (GTM), and a tau-specific GTM variant), seven choices of reference region (cerebellar crus, cerebellar gray matter, whole cerebellum, pons, supratentorial white matter, eroded supratentorial WM, and a composite of eroded supratentorial WM, pons, and whole cerebellum), two choices of region masking (GM or GM and WM), and two choices of statistic (voxel-wise mean vs. median). Our strongest findings were: 1) larger temporal-lobe target regions greatly outperformed entorhinal cortex (median sample size estimates based on a hypothetical clinical trial were 520–526 vs. 1740); 2) longitudinal processing pipelines outperformed cross-sectional pipelines (median sample size estimates were 483 vs. 572); and 3) reference regions including supratentorial WM outperformed traditional cerebellar and pontine options (median sample size estimates were 370 vs. 559). Altogether, our results favored longitudinally SUVR methods and a temporal-lobe meta-ROI that includes adjacent (juxtacortical) WM, a composite reference region (eroded supratentorial WM + pons + whole cerebellum), 2-class voxel-based PVC, and median statistics.

Disproportionately enlarged subarachnoid-space hydrocephalus (DESH), characterized by tight high convexity CSF spaces, ventriculomegaly, and enlarged Sylvian fissures, is thought to be an indirect marker of a CSF dynamics disorder. The clinical significance of DESH with regard to cognitive decline in a community setting is not yet well defined. The goal of this work is to determine if DESH is associated with cognitive decline. Participants in the population-based Mayo Clinic Study of Aging (MCSA) who met the following criteria were included: age ≥ 65 years, 3T MRI, and diagnosis of cognitively unimpaired or mild cognitive impairment at enrollment as well as at least one follow-up visit with cognitive testing. A support vector machine based method to detect the DESH imaging features on T1-weighted MRI was used to calculate a “DESH score”, with positive scores indicating a more DESH-like imaging pattern. For the participants who were cognitively unimpaired at enrollment, a Cox proportional hazards model was fit with time defined as years from enrollment to first diagnosis of mild cognitive impairment or dementia, or as years to last known cognitively unimpaired diagnosis for those who did not progress. Linear mixed effects models were fit among all participants to estimate annual change in cognitive z scores for each domain (memory, attention, language, and visuospatial) and a global z score. For all models, covariates included age, sex, education, APOE genotype, cortical thickness, white matter hyperintensity volume, and total intracranial volume. The hazard of progression to cognitive impairment was an estimated 12% greater for a DESH score of +1 versus −1 (HR 1.12, 95% CI 0.97–1.31, p = 0.11). Global and attention cognition declined 0.015 (95% CI 0.005–0.025) and 0.016 (95% CI 0.005–0.028) z/year more, respectively, for a DESH score of +1 vs −1 (p = 0.01 and p = 0.02), with similar, though not statistically significant DESH effects in the other cognitive domains. Imaging features of disordered CSF dynamics are an independent predictor of subsequent cognitive decline in the MCSA, among other well-known factors including age, cortical thickness, and APOE status. Therefore, since DESH contributes to cognitive decline and is present in the general population, identifying individuals with DESH features may be important clinically as well as for selection in clinical trials.

Recent advances in automated face recognition algorithms have increased the risk that de-identified research MRI scans may be re-identifiable by matching them to identified photographs using face recognition. A variety of software exist to de-face (remove faces from) MRI, but their ability to prevent face recognition has never been measured and their image modifications can alter automated brain measurements. In this study, we compared three popular de-facing techniques and introduce our mri_reface technique designed to minimize effects on brain measurements by replacing the face with a population average, rather than removing it. For each technique, we measured 1) how well it prevented automated face recognition (i.e. effects on exceptionally-motivated individuals) and 2) how it altered brain measurements from SPM12, FreeSurfer, and FSL (i.e. effects on the average user of de-identified data). Before de-facing, 97% of scans from a sample of 157 volunteers were correctly matched to photographs using automated face recognition. After de-facing with popular software, 28-38% of scans still retained enough data for successful automated face matching. Our proposed mri_reface had similar performance with the best existing method (fsl_deface) at preventing face recognition (28-30%) and it had the smallest effects on brain measurements in more pipelines than any other, but these differences were modest.

Altered iron metabolism has been hypothesized to be associated with Alzheimer's disease pathology, and prior work has shown associations between iron load and beta amyloid plaques. Quantitative susceptibility mapping (QSM) is a recently popularized MR technique to infer local tissue susceptibility secondary to the presence of iron as well as other minerals. Greater QSM values imply greater iron concentration in tissue. QSM has been used to study relationships between cerebral iron load and established markers of Alzheimer's disease, however relationships remain unclear. In this work we study QSM signal characteristics and associations between susceptibility measured on QSM and established clinical and imaging markers of Alzheimer's disease. The study included 421 participants (234 male, median age 70 years, range 34–97 years) from the Mayo Clinic Study of Aging and Alzheimer's Disease Research Center; 296 (70%) had a diagnosis of cognitively unimpaired, 69 (16%) mild cognitive impairment, and 56 (13%) amnestic dementia. All participants had multi-echo gradient recalled echo imaging, PiB amyloid PET, and Tauvid tau PET. Variance components analysis showed that variation in cortical susceptibility across participants was low. Linear regression models were fit to assess associations with regional susceptibility. Expected increases in susceptibility were found with older age and cognitive impairment in the deep and inferior gray nuclei (pallidum, putamen, substantia nigra, subthalamic nucleus) (betas: 0.0017 to 0.0053 ppm for a 10 year increase in age, p = 0.03 to

Progressive supranuclear palsy is a neurodegenerative disorder characterized primarily by tau inclusions and neurodegeneration in the midbrain, basal ganglia, thalamus, premotor and frontal cortex. Neurodegenerative change in progressive supranuclear palsy has been assessed using MRI. Degeneration of white matter tracts is evident with diffusion tensor imaging and PET methods have been used to assess brain metabolism or presence of tau protein deposits. Patients with progressive supranuclear palsy present with a variety of clinical syndromes; however early onset of gait impairments and postural instability are common features. In this study we assessed the relationship between multimodal imaging biomarkers (i.e., MRI atrophy, white matter tracts degeneration, flortaucipir-PET uptake) and laboratory-based measures of gait and balance abnormalities in a cohort of nineteen patients with progressive supranuclear palsy, using univariate and multivariate statistical analyses. The PSP rating scale and its gait midline sub-score were strongly correlated to gait abnormalities but not to postural imbalance. Principal component analysis on gait variables identified velocity, stride length, gait stability ratio, length of gait phases and dynamic stability as the main contributors to the first component, which was associated with diffusion tensor imaging measures in the posterior thalamic radiation, external capsule, superior cerebellar peduncle, superior fronto-occipital fasciculus, body and splenium of the corpus callosum and sagittal stratum, with MRI volumes in frontal and precentral regions and with flortaucipir-PET uptake in the precentral gyrus. The main contributor to the second principal component was cadence, which was higher in patients presenting more abnormalities on mean diffusivity: this unexpected finding might be related to compensatory gait strategies adopted in progressive supranuclear palsy. Postural imbalance was the main contributor to the third principal component, which was related to flortaucipir-PET uptake in the left paracentral lobule and supplementary motor area and white matter disruption in the superior cerebellar peduncle, putamen, pontine crossing tract and corticospinal tract. A partial least square model identified flortaucipir-PET uptake in midbrain, basal ganglia and thalamus as the main correlate of speed and dynamic component of gait in progressive supranuclear palsy. Although causality cannot be established in this analysis, our study sheds light on neurodegeneration of brain regions and white matter tracts that underlies gait and balance impairment in progressive supranuclear palsy.

Abstract The Worldwide Alzheimer's Disease Neuroimaging Initiative (WW‐ADNI) is a collaborative effort to investigate imaging and biofluid markers that can inform Alzheimer's disease treatment trials. It is a public‐private partnership that spans North America, Argentina, Australia, Canada, China, Japan, Korea, Mexico, and Taiwan. In 2004, ADNI researchers began a naturalistic, longitudinal study that continues today around the globe. Through several successive phases (ADNI‐1, ADNI‐GO, ADNI‐2, and ADNI‐3), the study has fueled amyloid and tau phenotyping and refined neuroimaging methodologies. WW‐ADNI researchers have successfully standardized analyses and openly share data without embargo, providing a rich data set for other investigators. On August 26, 2020, the Alzheimer's Association convened WW‐ADNI researchers who shared updates from ADNI‐3 and their vision for ADNI‐4.

Disproportionately enlarged subarachnoid space hydrocephalus (DESH), characterized by ventriculomegaly, high convexity/midline tight sulci, and enlarged sylvian fissures on brain MRI has been increasingly recognized as a distinct diagnostic imaging entity that falls within the larger category of idiopathic normal pressure hydrocephalus. Normal pressure hydrocephalus has been previously characterized as a CSF dynamics disorder based on abnormalities on nuclear medicine cisternography: radiotracer in the lateral ventricles and absent or delayed ascent of radiotracer over the cerebral convexity. The purpose of this work was to evaluate for differences in nuclear medicine cisternography between patients with vs without DESH and thereby provide support for the concept that DESH is a structural imaging marker of a CSF dynamics disorder. The study included 102 patients (mean age 71 years, range 46–86, 38 females), 58 patients with cisternogram performed to evaluate suspected normal pressure hydrocephalus (mean age 73 years, range 46–86 years, 24 female) and 44 patients evaluated for headache (mean age 68 years, range 60–82 years, 14 female). All patients had an MRI of the brain performed within 13 months of the cisternogram. Cisternogram imaging, typically acquired at 0.5, 1, 2, 4, and 24 h post injection, was evaluated for the time at which radiotracer reached the basal cisterns, presence of persistent radiotracer in the lateral ventricles, time radiotracer first entered the lateral ventricles, presence of radiotracer over the cerebral convexity, and time at which radiotracer was first visualized over the cerebral convexity. MRI features of ventriculomegaly (defined as Evans’ index ≥ 0.3) and high convexity tight sulci (HCTS) were recorded. Based on the MRI features, patients were grouped according to presence or absence of DESH (ventriculomegaly and HCTS). Those without DESH were separated into groups of ventriculomegaly alone, HCTS alone, and neither ventriculomegaly nor HCTS. Cisternogram metrics were compared between MR-defined groups. Patients with DESH showed a higher frequency of radiotracer in the lateral ventricles and delayed or absent ascent over the cerebral convexity compared to those without DESH, higher frequency of ventricular radioactivity vs those with HCTS alone, and shorter time to ventricular radioactivity compared to those with ventriculomegaly alone. Patients with ventriculomegaly or HCTS alone had a higher frequency of radiotracer in the lateral ventricles and delayed ascent of radiotracer over the cerebral convexity compared to those with neither ventriculomegaly nor HCTS. These findings support DESH and the individual components of ventriculomegaly and HCTS as markers of disordered CSF dynamics.

Defining a signature of cortical regions of interest preferentially affected by Alzheimer disease (AD) pathology may offer improved sensitivity to early AD compared to hippocampal volume or mesial temporal lobe alone. Since late-onset Alzheimer disease (LOAD) participants tend to have age-related comorbidities, the younger-onset age in autosomal dominant AD (ADAD) may provide a more idealized model of cortical thinning in AD. To test this, the goals of this study were to compare the degree of overlap between the ADAD and LOAD cortical thinning maps and to evaluate the ability of the ADAD cortical signature regions to predict early pathological changes in cognitively normal individuals.We defined and analyzed the LOAD cortical maps of cortical thickness in 588 participants from the Knight Alzheimer Disease Research Center (Knight ADRC) and the ADAD cortical maps in 269 participants from the Dominantly Inherited Alzheimer Network (DIAN) observational study. Both cohorts were divided into three groups: cognitively normal controls (nADRC = 381; nDIAN = 145), preclinical (nADRC = 153; nDIAN = 76), and cognitively impaired (nADRC = 54; nDIAN = 48). Both cohorts underwent clinical assessments, 3T MRI, and amyloid PET imaging with either 11C-Pittsburgh compound B or 18F-florbetapir.To generate cortical signature maps of cortical thickness, we performed a vertex-wise analysis between the cognitively normal controls and impaired groups within each cohort using six increasingly conservative statistical thresholds to determine significance. The optimal cortical map among the six statistical thresholds was determined from a receiver operating characteristic analysis testing the performance of each map in discriminating between the cognitively normal controls and preclinical groups. We then performed within-cohort and cross-cohort (e.g. ADAD maps evaluated in the Knight ADRC cohort) analyses to examine the sensitivity of the optimal cortical signature maps to the amyloid levels using only the cognitively normal individuals (cognitively normal controls and preclinical groups) in comparison to hippocampal volume.We found the optimal cortical signature maps were sensitive to early increases in amyloid for the asymptomatic individuals within their respective cohorts and were significant beyond the inclusion of hippocampus volume, but the cortical signature maps performed poorly when analyzing across cohorts. These results suggest the cortical signature maps are a useful MRI biomarker of early AD-related neurodegeneration in preclinical individuals and the pattern of decline differs between LOAD and ADAD.

Background: Idiopathic normal pressure hydrocephalus (iNPH) is an important and treatable cause of neurologic impairment. Diagnosis is complicated due to symptoms overlapping with other age related disorders. The pathophysiology underlying iNPH is not well understood. We explored FDG-PET abnormalities in iNPH patients in order to determine if FDG-PET may serve as a biomarker to differentiate iNPH from common neurodegenerative disorders. Methods: We retrospectively compared 18F-FDG PET-CT imaging patterns from seven iNPH patients (mean age 74 ± 6 years) to age and sex matched controls, as well as patients diagnosed with clinical Alzheimer's disease dementia (AD), Dementia with Lewy Bodies (DLB) and Parkinson's Disease Dementia (PDD), and behavioral variant frontotemporal dementia (bvFTD). Partial volume corrected and uncorrected images were reviewed separately. Results: Patients with iNPH, when compared to controls, AD, DLB/PDD, and bvFTD, had significant regional hypometabolism in the dorsal striatum, involving the caudate and putamen bilaterally. These results remained highly significant after partial volume correction. Conclusions: In this study, we report a FDG-PET pattern of hypometabolism in iNPH involving the caudate and putamen with preserved cortical metabolism. This pattern may differentiate iNPH from degenerative diseases and has the potential to serve as a biomarker for iNPH in future studies. These findings also further our understanding of the pathophysiology underlying the iNPH clinical presentation. Keywords: FDG-PET, Normal pressure hydrocephalus, Hypometabolism, Caudate, Biomarker

Abstract Introduction Medial temporal lobe (MTL) uptake on tau–positron emission tomography (PET) is seen not only in Alzheimer's disease (AD) dementia but also in the aging population. The relationship of these findings to the development of AD dementia needs to be better understood. Methods Tau‐PET with AV‐1451 was performed on 576 cognitively unimpaired (CU) participants aged 50–94 years. The number of CUs with and without abnormal MTL regions and those with or without extra‐MTL abnormalities was determined. Left and right regions were compared within each subject. Results Of CUs, 58% (334/576) had abnormal tau‐PET findings. MTL abnormalities were present in 41% (238/576) of subjects. Discussion MTL tau‐PET signal is often associated with abnormal extra‐MTL tau‐PET signal in CU participants and may represent neurofibrillary tangle development that could identify participants most likely to develop AD dementia. Tau‐PET signal exclusively outside of the MTL is seen in 17% of CU participants and could be the initial findings in participants in different AD dementia pathways. Significant (P

The hippocampus in mammalian brain varies in size across individuals. Here, Hibar and colleagues perform a genome-wide association meta-analysis to find six genetic loci with significant association to hippocampus volume.

Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter (WM) changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer’s Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4 ± 7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FADTI) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FATDF), in 24 bilaterally averaged WM regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FADTI. We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and AD: the AD Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects regression model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix (crus)/stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum (CGH) and uncinate fasciculus (UNC) for associations between axial or mean diffusivity and CDR-sob. FATDF detected robust widespread associations with clinical measures, while FADTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.

The aims of this study were: to examine regional rates of change in tau-PET uptake and grey matter volume in atypical Alzheimer's disease (AD); to investigate the role of age in such changes; to describe multimodal regional relationships between tau accumulation and atrophy. Thirty atypical AD patients underwent baseline and one-year follow-up MRI, [18F]AV-1451 PET and PiB PET. Region- and voxel-level rates of tau accumulation and grey matter atrophy relative to cognitively unimpaired individuals, and the influence of age on such rates, were assessed. Univariate and multivariate analyses were performed between baseline measurements and rates of change, between baseline tau and atrophy, and between the two rates of change. Regional patterns of change in tau and volume differed, with highest rates of tau accumulation in frontal lobe and highest rates of atrophy in temporoparietal regions. Age had a negative effect on disease progression, predominantly on tau, with younger patients having a more rapid accumulation. Baseline tau uptake and regions of tau accumulation were disconnected, with high baseline tau uptake across the cortex correlated with high rates of tau accumulation in frontal and sensorimotor regions. In contrast, baseline volume and atrophy were locally related in the occipitoparietal regions. Higher tau uptake at baseline was locally related to higher rates of atrophy in frontal and occipital lobes. Tau accumulation rates positively correlated with rates of atrophy. In summary, our study showed that tau accumulation and atrophy presented different regional patterns in atypical AD, with tau spreading into the frontal lobes while atrophy remains in temporoparietal and occipital cortex, suggesting a temporal disconnect between protein deposition and neurodegeneration. Keywords: Longitudinal tau-PET, Atrophy, Atypical AD, Multimodal imaging

Objective: Create an automated classifier for imaging characteristics of disproportionately enlarged subarachnoid space hydrocephalus (DESH), a neuroimaging phenotype of idiopathic normal pressure hydrocephalus (iNPH). Methods: 1597 patients from the Mayo Clinic Study of Aging (MCSA) were reviewed for imaging characteristics of DESH. One core feature of DESH, the presence of tightened sulci in the high-convexities (THC), was used as a surrogate for the presence of DESH as the expert clinician-defined criterion on which the classifier was trained. Anatomical MRI scans were automatically segmented for cerebrospinal fluid (CSF) and overlaid with an atlas of 123 named sulcal regions. The volume of CSF in each sulcal region was summed and normalized to total intracranial volume. Area under the receiver operating characteristic curve (AUROC) values were computed for each region individually, and these values determined feature selection for the machine learning model. Due to class imbalance in the data (72 selected scans out of 1597 total scans) adaptive synthetic sampling (a technique which generates synthetic examples based on the original data points) was used to balance the data. A support vector machine model was then trained on the regions selected. Results: Using the automated classification model, we were able to classify scans for tightened sulci in the high convexities, as defined by the expert clinician, with an AUROC of about 0.99 (false negative ≈ 2%, false positive ≈ 5%). Ventricular volumes were among the classifier's most discriminative features but are not specific for DESH. The inclusion of regions outside the ventricles allowed specificity from atrophic neurodegenerative diseases that are also accompanied by ventricular enlargement. Conclusion: Automated detection of tight high convexity, a key imaging feature of DESH, is possible by using support vector machine models with selected sulcal CSF volumes as features. Keywords: Normal pressure ydrocephalus, Disproportionately enlarged subarachnoid hydrocephalus, Support vector machines, Computer-aided diagnosis, Tight high-convexity

Magnetic resonance elastography (MRE) is an MRI-based technique to noninvasively measure tissue stiffness. Currently well established for clinical use in the liver, MRE is increasingly being investigated to measure brain stiffness as a novel biomarker of a variety of neurological diseases. The purpose of this work was to apply a recently developed MRE pipeline to measure regional brain stiffness changes in human subjects across the Alzheimer's disease (AD) spectrum, and to gain insights into the biological processes underlying those stiffness changes by correlating stiffness with existing biomarkers of AD. The results indicate that stiffness changes occur mostly in the frontal, parietal and temporal lobes, in accordance with the known topography of AD pathology. Furthermore, stiffness in those areas correlates with existing imaging biomarkers of AD including hippocampal volumes and amyloid PET. Additional analysis revealed preliminary but significant evidence that the relationship between brain stiffness and AD severity is nonlinear and non-monotonic. Given that similar relationships have been observed in functional MRI experiments, we used task-free fMRI data to test the hypothesis that brain stiffness was sensitive to structural changes associated with altered functional connectivity. The analysis revealed that brain stiffness is significantly and positively correlated with default mode network connectivity. Therefore, brain stiffness as measured by MRE has potential to provide new and essential insights into the temporal dynamics of AD, as well as the relationship between functional and structural plasticity as it relates to AD pathophysiology.

Biomarkers are the only feasible way to detect and monitor presymptomatic Alzheimer's disease (AD). No single biomarker can predict future cognitive decline with an acceptable level of accuracy. In addition to designing powerful multimodal diagnostic platforms, a careful investigation of the major sources of disease heterogeneity and their influence on biomarker changes is needed. Here we investigated the accuracy of a novel multimodal biomarker classifier for differentiating cognitively normal (NC), mild cognitive impairment (MCI) and AD subjects with and without stratification by ApoE4 genotype. 111 NC, 182 MCI and 95 AD ADNI participants provided both structural MRI and CSF data at baseline. We used an automated machine-learning classifier to test the ability of hippocampal volume and CSF Aβ, t-tau and p-tau levels, both separately and in combination, to differentiate NC, MCI and AD subjects, and predict conversion. We hypothesized that the combined hippocampal/CSF biomarker classifier model would achieve the highest accuracy in differentiating between the three diagnostic groups and that ApoE4 genotype will affect both diagnostic accuracy and biomarker selection. The combined hippocampal/CSF classifier performed better than hippocampus-only classifier in differentiating NC from MCI and NC from AD. It also outperformed the CSF-only classifier in differentiating NC vs. AD. Our amyloid marker played a role in discriminating NC from MCI or AD but not for MCI vs. AD. Neurodegenerative markers contributed to accurate discrimination of AD from NC and MCI but not NC from MCI. Classifiers predicting MCI conversion performed well only after ApoE4 stratification. Hippocampal volume and sex achieved AUC = 0.68 for predicting conversion in the ApoE4-positive MCI, while CSF p-tau, education and sex achieved AUC = 0.89 for predicting conversion in ApoE4-negative MCI. These observations support the proposed biomarker trajectory in AD, which postulates that amyloid markers become abnormal early in the disease course while markers of neurodegeneration become abnormal later in the disease course and suggests that ApoE4 could be at least partially responsible for some of the observed disease heterogeneity.

Smart molecular probes for both diagnostic and therapeutic purposes are expected to provide significant advances in clinical medicine and biomedical research. We describe such a probe that targets β-amyloid plaques of Alzheimer's disease and is detectable by magnetic resonance imaging (MRI) because of contrast imparted by gadolinium labeling. Three properties essential for contrast enhancement of β-amyloid plaques on MRI exist in this smart molecular probe, putrescine–gadolinium–amyloid-β peptide: (1) transport across the blood–brain barrier following intravenous injection conferred by the polyamine moiety, (2) binding to plaques with molecular specificity by putrescine–amyloid-β, and (3) magnetic resonance imaging contrast by gadolinium. MRI was performed on ex vivo tissue specimens at 7 T at a spatial resolution approximating plaque size (62.5 μm3), in order to prove the concept that the probe, when administered intravenously, can selectively enhance plaques. The plaque-to-background tissue contrast-to-noise ratio, which was precisely correlated with histologically stained plaques, was enhanced more than nine-fold in regions of cortex and hippocampus following intravenous administration of this probe in AD transgenic mice. Continuing engineering efforts to improve spatial resolution are underway in MRI, which may enable in vivo imaging at the resolution of individual plaques with this or similar contrast probes. This could enable early diagnosis and also provide a direct measure of the efficacy of anti-amyloid therapies currently being developed.