Citations

AbstractBackgroundSlow walking speed is associated with numerous negative health outcomes including global cognitive decline.1 More detailed gait analysis can differentiate among different dementia subtypes,10 but the predictive power of baseline gait metrics for domain‐specific cognitive decline needs further characterization.MethodWe conducted a follow up study on 534 participants of the Mayo Clinic Study of Aging who underwent evaluations every 15 months (median follow‐up = 5.4 years). We analyzed quantitative gait metrics, acquired with a pressure‐sensing walkway (GAITRite® instrument), and cognitive scores using a linear mixed effects model to identify correlations between z‐scored baseline gait metrics (cadence, swing time, stance time, velocity, double support time (DST), stride length, stride length standard deviation (SD), stride time SD, swing time SD, stance time SD, DST SD) and cognitive domain z‐scores (memory, attention, language, visuospatial, global) at baseline and over time. Models controlled for age, sex, and probable REM sleep behavior disorder at baseline.ResultThe table includes both baseline associations and difference in slopes (Δ), relative to the average person, in cognitive domains. These slopes ranged from about ‐0.05 for visuospatial to ‐0.12 for attention.ConclusionIncreased gait velocity and stride length are associated with less severe domain‐specific and global cognitive decline while increased swing time SD and DST are associated with more severe rates of decline in some domains. These findings suggest that baseline gait characteristics could be a helpful predictor of future cognitive changes.References:1. Savica R, Wennberg AMV, Hagen C, et al. Comparison of Gait Parameters for Predicting Cognitive Decline: The Mayo Clinic Study of Aging. Journal of Alzheimer's disease : JAD. 2017;55(2):559. doi:10.3233/JAD‐1606972. Mc Ardle R, del Din S, Galna B, Thomas A, Rochester L. Differentiating dementia disease subtypes with gait analysis: feasibility of wearable sensors? Gait & Posture. 2020;76:372–376. doi:10.1016/j.gaitpost.2019.12.028

AbstractBackgroundCognitive resilience, the discrepancy observed in individuals with high Alzheimer’s disease (AD) neuropathology and near‐normal cognition, is a growing area of study, wherein multiple factors are responsible. Currently, the role of the microglial activation in cognitive resilience is unknown. Herein described is a case report characterizing the amyloid responsive microglia (ARM) in an 88‐year‐old male decedent homozygous for APOE E4, clinically cognitively intact, and found to have AD neuropathologic change (ADNC) upon autopsy.MethodThe participant was enrolled in the Mayo Clinic Study of Aging (MCSA) and continuously followed for 14 years from 73‐ to 87‐years‐old, wherein serial MRI and clinical assessments were obtained. An autopsy was performed upon death, and routine neuropathologic sections were taken. Formalin‐fixed, paraffin‐embedded sections were stained with H&E, and single immunohistochemistry was performed using antibodies against tau (AT8), beta‐amyloid (6F3D), alpha‐synuclein (LB509), TDP‐43 (pS409/410), and CD163 (10D6). Digital image analysis was performed on dorsolateral prefrontal cortex sections scanned at 40x (Leica GT450). ARM (CD163% area) and beta‐amyloid (% area) measurements were obtained using QuPath threshold classifiers. Descriptive statistics were performed (Graphpad).ResultThe decedent was an 88‐year‐old male, homozygous for APOE E4, with a history of three cardiovascular and metabolic conditions, and lifelong smoking. His MMSE (average 28/30) and global cognition (average ‐0.51) were relatively stable (Figure 1), with no clinical concerns for cognitive impairment. Cortical thickness was normal (temporal meta‐ROI>3 mm) on four serial MRIs. The participant reported high cognitive, physical activities, and education‐occupation scores (upper quartile for the 70+ population). Neuropathologic evaluation was notable for severe cerebral amyloid angiopathy, high ADNC (A3B3C3), and remote microinfarcts. CD163‐positive ARM were observed in the neocortices and perivascular spaces, and the ARM:beta‐amyloid ratio was 0.26 (Figure 1). This ARM:beta‐amyloid ratio trended upwards (>90th percentile) compared to APOE E4/E4‐matched‐ and age‐matched controls provided from the University of Pennsylvania.ConclusionThis case report demonstrates a cognitively resilient individual with an elevated ARM:amyloid ratio. Conversely, lower ARM ratios were observed in a cognitively impaired cohort, raising the possibility ARM may associate with cognition. ARM may represent a protective and phagocytic population, and further studies correlating microglia, cognition, and putative mechanism are required.

AbstractBackgroundCerebral Microbleeds (CMBs) are tracked in anti‐amyloid drug safety monitoring. T2*GRE or SWI MRI images are visually assessed for the presence of CMBs. Automated methods tuned for sensitivity often have unacceptably high false positive rates requiring extensive human cleanup.MethodAssume contemporaneous T2*GRE and T1‐weighted images. CMB detection (Figures 1,2) has three steps: 1) Segmentation outputs from the T1‐weighted image are resampled into the T2*GRE image voxel raster; 2) Normalized cross‐correlation of the T2*GRE with a CMB template identifies “candidate CMBs”; 3) a neural network using voxel patches centered on each candidate CMB rejects false positives. Patches presented to the network are 4D and include five contrasts drawn from the T2*GRE, correlation, T1‐weighted, WM probability and CSF probability images. The simple network architecture consists of convolutional blocks followed by dense blocks. The system is trained using images from ADNI, ARIC, MCSA, and the Mayo Clinic ADRC that have been previously visually graded for CMBs (Table in Figure 3). Importantly, we include participants with and without CMBs in our data reflecting the fact that the majority of people (even the elderly) have no CMBs. Data is split 70:10:20 by subject for training, validation, and testing. Various network configurations were evaluated.ResultHigh candidate detection sensitivity can be achieved at the expense of presenting hundreds to thousands of false positive candidates per input image to the rejection network. Our best performing network configuration to date has AUC 0.982 (95% bootstrap CI 0.971 to 0.990 based on the 10% validation subset). Choosing an operating point with 95% sensitivity in the validation set and admits, on average, 0.6 false positive CMB candidates per input T2*GRE image. Sensitivity is hindered by a small number of obvious errors in the ground truth visual assessment. Validation loss tracks training loss with no indication of over‐fitting. Example patches are shown in Figure 4.ConclusionExcessive false positive detection requires significant human correction and effort, preventing automated CMB detection from being widely adopted. We have an automated detection system that is sensitive (95%) and provides excellent false positive rejection (<1 false positive CMB/input image) reducing needed human effort.

AbstractBackgroundIn 2018 we presented the Computational DESH (CDESH) metric (DOI:10.1016/j.nicl.2018.11.015), a machine learning measure of abnormal CSF space patterns commonly observed on MRI in Disproportionately Enlarged Subarachnoid‐space Hydrocephalus (DESH‐type) NPH. We have shown that CDESH positive individuals often have discordant CSF and PET amyloid measures (DOI:10.1016/j.jalz.2019.06.4921). Positive CDESH scores are predictive of cognitive decline (DOI:10.1016/j.neuroimage.2021.117899, DOI: 10.1212/WNL.0000000000008616). The score is calculated from regional CSF volumes in a sulcal atlas and within the ventricles using a support vector machine (SVM). The Mayo Clinic Study of Aging (MCSA) changed scanner platforms (GE to Siemens) and T1‐weighted image resolution (1.2mm x 1.05mm x 1.05mm to 0.8mm isotropic) in 2017. We refer to these as “isotropic” or “anisotropic” scans. Our objective was to assess the dependence of a machine learning method (specifically CDESH) to image input variation and, if necessary, determine what needed corrections.MethodAll data considered is from the MCSA or Mayo Clinic ADRC. The SVM was trained on anisotropic images with laboriously labelled ground truth. We resampled the isotropic data from a crossover study (N=112, typically scanned same‐day with both platforms/resolutions) to match the anisotropic resolution. Two approaches to correction were evaluated (Figure 1). In Figure 2 we compare of the score with and without correction cross‐sectionally in 998 MCSA participants. In Figure 3 we present longitudinal series from 23 participants with multiple scans using the isotropic resolution protocol and at least one prior anisotropic image.ResultsComparisons of data from the crossover study are presented in Figure 1. Matching voxel resolution to the data used to train the SVM is critical and a subsequent mild linear score adjustment may be warranted. Cross‐sectional comparisons (Figure 2) confirm improvement matching voxel resolution with a possible mild linear score adjustment. After matching voxel resolution CDESH score trajectories (Figure 3) are markedly more stable across this scanner/protocol change.ConclusionThe SVM‐based CDESH score is sensitive to voxel resolution. Corrections are needed in image processing and possibly in the scalar output score to achieve stability across protocol and scanner changes. The resulting correction supports coherent long timeseries data and longitudinal extension of previous work.

AbstractBackgroundFluorodeoxyglucose positron emission tomography (FDG‐PET) is an established modality for differential diagnosis of dementia. Deriving phenotypic signatures from FDG‐PET via machine learning is challenging due to the high dimensional nature of brain images relative to the generally small number of labeled examples available for training, the class imbalance among those labels, and the cooccurrence of multiple pathologies. In this study, we developed a multi‐class, multi‐label framework to address these challenges.MethodA database of clinically acquired PET/CT images from 3,000 unique patients was used to develop a latent space model using matrix decomposition. This model was then applied to images from a separate cohort of Mayo Clinic Alzheimer’s Disease Research Center participants (n=1,745) labeled as cognitively unimpaired (CU) (n=1,436) or with the following potentially co‐occurring phenotypes: Alzheimer’s disease (AD) (n=165), Lewy body dementia (DLB) (n=92), behavioral variant frontotemporal dementia (bvFTD) (n=43), semantic (svPPA) (n=10) and logopenic (lvPPA) (n=13) variant PPA, and posterior cortical atrophy (PCA) (n=17). A k‐nearest neighbors classifier that is robust to these imbalanced and overlapping labels was then trained on these examples. The resulting classifier was evaluated by area under receiver‐operator characteristic curve (ROC‐AUC) via leave one out cross validation, using clinical diagnosis as the gold standard.ResultROC curves and AUC scores for each phenotype are illustrated in Fig. 1a. Because the classifier is based on a k‐nearest neighbors connectivity matrix, it has a convenient graphical representation, where images are nodes and edges are drawn between an image and its set of nearest neighbors in latent space. A self‐organizing force directed graph constructed in this way is illustrated in Fig 1b, highlighting the strong separation of CU and degenerative images, as well as the segregation of each phenotype within the neurodegenerative region of the graph.ConclusionIn this study, we developed a machine learning framework for classification of neurodegenerative disease based on k‐nearest neighbor analysis in a low dimensional latent space projection of FDG‐PET images. By leveraging low‐dimensional representations and k‐nearest neighbors analysis, this framework is robust in multi‐class, multi‐label tasks with strong class imbalance and provides a highly interpretable graphical representation.

AbstractBackgroundWhite matter hyperintensities (WMH) are markers of cerebral small vessel disease and have been associated with higher risk of typical amnestic Alzheimer’s disease (AD), however, little is known about the role of WMHs in atypical variants of AD. This study aimed to determine the frequency and distribution of WMH in two atypical variants of AD: logopenic progressive aphasia (LPA) and posterior cortical atrophy (PCA), and determine whether WMH are associated with demographic and clinical outcomes in these cohorts.MethodSeventy‐five LPA and 39 PCA patients were recruited by the Neurodegenerative Research Group and underwent two‐ dimensional fluid‐attenuated inversion recovery (FLAIR) MRI and Pittsburgh Compound B (PiB) PET. All patients were PiB‐positive. All FLAIR images were segmented using an in‐house software, then were manually edited by a trained imaging analyst. WMH volume was scaled by total intracranial volume to correct for differences in head size. Adjusted WMH burdens were compared between LPA and PCA, and associations were assessed between WMH burden and age, global PiB standard uptake value ratios (SUVRs), and clinical metrics, including tests of general cognitive impairment, executive function, naming, repetition, visuospatial and visuoperceptual function, and parkinsonism. Associations between WMHs and clinical scores were adjusted for age and brain volume to correct for atrophy.ResultTotal WMH burden was similar in PCA and LPA, however, regional differences were identified with PCA having greater subcortical WMH burden in right occipital, parietal, and temporal lobes compared to LPA, and LPA having greater WMH burden in left deep grey and white matter and greater subcortical burden in left parietal lobe compared to PCA. Total WMH burden was greater with older age in both groups but was not related to global PiB SUVR either globally or locally. Greater total WMH was associated with visuoperceptual performance in both cohorts after correcting for atrophy. When assessed regionally, visuoperceptual performance was associated with right subcortical occipital WMH across the cohort.ConclusionRegional distribution of WMHs differ across clinical variants of atypical AD and WMHs may impact clinical outcomes, although further work is needed to determine whether they reflect cerebrovascular disease or regionally specific neurodegenerative changes.

AbstractBackgroundResting state functional connectivity (rsfMRI) studies have shown functional network disruptions in logopenic progressive aphasia (LPA) (reduced connectivity within the left temporal language network) and posterior cortical atrophy (PCA) (reduced connectivity within ventral visual network). However, little is known about how these networks compare and how connectivity differs within and between networks in these atypical Alzheimer’s Disease (AD) phenotypes.MethodFifty‐nine amyloid‐positive PCA patients and eighty‐five amyloid‐positive LPA patients were recruited by the Neurodegenerative Research Group at Mayo Clinic, Rochester, MN, and underwent structural and resting state functional MRI. Spatially preprocessed data was analyzed using the CONN functional connectivity toolbox to explore the default mode network (DMN), salience, sensorimotor, left language, visual and memory networks, which were generated by averaging multiple regions‐of‐interest (ROI) within each network. The data was analyzed at both the voxel‐level and network‐level (ROI‐ROI).ResultVoxel‐level differences in functional connectivity between PCA and LPA were observed in all networks (p<0.05; cluster size, FWE=0.05). PCA showed reduced connectivity in the occipital lobe in visual and memory networks, parietal lobe in language and DMN, and sensorimotor strip in sensorimotor network, particularly on the right, compared to LPA. Conversely, LPA showed reduced connectivity in the frontal cortex in salience, memory, visual and sensorimotor networks, inferior parietal lobe in visual network, and anterior temporal pole and sensorimotor cortex in the language network, compared to PCA. Between network differences (p<0.05; cluster size, FDR=0.05) were observed between groups, with PCA showing reduced connectivity between the visual and sensorimotor networks, while LPA showed reduced connectivity between the language and sensorimotor networks. The DMN was anti‐correlated to the language network in PCA, while it was positively correlated to the language network in LPA. In both phenotypes, the DMN was anticorrelated with the salience network. The visual network in PCA showed connections to all networks, while in LPA, it was only connected to the salience, language and DMN.ConclusionThe atypical AD phenotypes were associated with multiple disruptions in connectivity, both within and between brain networks. Phenotype specific differences in connectivity patterns were noted in visual network for PCA and language network for LPA.

AbstractBackgroundQuantitative susceptibility mapping (QSM) can detect iron distribution in the brain by estimating the local tissue magnetic susceptibility of every voxel. Dysregulation of neuronal iron has been correlated with typical Alzheimer’s disease (tAD), but little is known about iron distribution in atypical clinical presentations of AD such as logopenic progressive aphasia (LPA) and posterior cortical atrophy (PCA).MethodEleven PCA patients and eight LPA patients were recruited by the Neurodegenerative Research Group at Mayo Clinic, Rochester, MN, and underwent MRI that included a five‐echo gradient echo sequence for calculation of QSM. Mean QSM signal was extracted from gray and white matter for regions‐of‐interest across the brain using the Mayo Clinic Adult Lifespan Template. Hierarchical models were fit per‐region and per‐hemisphere to compare PCA and LPA to each other, to 20 healthy controls, and to 20 patients with tAD recruited by the Alzheimer’s Disease Research Center.ResultStrong evidence (posterior probability>0.99) was observed for greater susceptibility in the left middle occipital gyrus and amygdala in both LPA and PCA, and in the right precuneus and inferior temporal gyrus in PCA compared to controls. Moderate evidence for greater susceptibility (posterior probability>0.90) was also observed in the left insula, inferior occipital gyrus, entorhinal cortex, and right inferior parietal gyrus and amygdala in both LPA and PCA, along with right angular gyrus and middle occipital gyrus in PCA and right insula and caudate in LPA, when compared to controls. Comparing between phenotypes, LPA showed moderate evidence for greater susceptibility in the caudate, hippocampus, substantia nigra, left supramarginal gyrus and posterior cingulate compared to PCA, while PCA showed greater susceptibility in the right precuneus compared to LPA. Both LPA and PCA showed moderate‐strong evidence for greater susceptibility than tAD, particularly in medial and lateral parietal regions.ConclusionUnique iron profiles were identified in LPA and PCA within cortical and subcortical structures. These changes are in line with disease‐related neurodegenerative changes in the clinical phenotypes, suggesting that QSM could be an informative marker to study iron deposition in these patients.

AbstractBackgroundGlobular glial tauopathy (GGT) has been associated with frontotemporal dementia syndromes; little is known about the clinical and imaging characteristics of GGT and how they differ from other non‐globular glial 4‐repeat tauopathies (N4GT) such as progressive supranuclear palsy (PSP) or corticobasal degeneration (CBD).MethodsFor this case–control study the Mayo Clinic brain banks were queried for all cases with an autopsy‐confirmed diagnosis of GGT between 1 January 2011 and 31 October 2021. Fifty patients with N4GT (30 PSP, 20 CBD) were prospectively recruited and followed by the Neurodegenerative Research Group at Mayo Clinic, Minnesota. Magnetic resonance imaging was used to characterize patterns of gray/white matter atrophy, MR‐parkinsonism index, midbrain volume, and white matter hyperintensities.18F‐Fluorodeoxyglucose‐, 11C Pittsburg compound‐, and 18F‐flortaucipir‐positron emission tomography scans were reviewed.ResultsTwelve patients with GGT were identified: 83% were women compared to 42% in NG4T (p = 0.02) with median age at death 76.5 years (range: 55–87). The most frequent clinical features were eye movement abnormalities, parkinsonism, behavioral changes followed by pyramidal tract signs and motor speech abnormalities. Lower motor neuron involvement was present in 17% and distinguished GGT from NG4T (p = 0.035). Primary progressive apraxia of speech was the most frequent initial diagnosis (25%); 50% had a Parkinson‐plus syndrome before death. Most GGT patients had asymmetric frontotemporal atrophy with matching hypometabolism. GGT patients had more gray matter atrophy in temporal lobes, normal MR‐parkinsonism index, and larger midbrain volumes.ConclusionsFemale sex, lower motor neuron involvement in the context of a frontotemporal dementia syndrome, and asymmetric brain atrophy with preserved midbrain might be suggestive of underlying GGT.