Graff‐Radford, Jonathan

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.

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.

Background This study aimed to compare percutaneous left atrial appendage occlusion (LAAO) with non‐vitamin K antagonist oral anticoagulants among patients with atrial fibrillation. Methods and Results Using a US administrative database, 562 850 patients with atrial fibrillation were identified, among whom 8397 were treated with LAAO and 554 453 were treated with non‐vitamin K antagonist oral anticoagulants between March 13, 2015 and December 31, 2018. Propensity score overlap weighting was used to balance baseline characteristics. The primary outcome was a composite end point of ischemic stroke or systemic embolism, major bleeding, and all‐cause mortality. The mean age was 76.4±7.6 years; 280 097 (49.8%) were female. Mean follow‐up was 1.5±1.0 years. LAAO was associated with no significant difference in the risk of the primary composite end point (hazard ratio [HR], 0.93 [0.84–1.03]), or the secondary outcomes including ischemic stroke/systemic embolism (HR, 1.07 [0.81–1.41]), and intracranial bleeding (HR, 1.08 [0.72–1.61]). LAAO was associated with a higher risk of major bleeding (HR, 1.22 [1.05–1.42], P =0.01) and a lower risk of mortality (HR, 0.73 [0.64–0.84], P <0.001). The lower risk of mortality associated with LAAO was most pronounced in patients with a prior history of intracranial bleeding. Conclusions In comparison to non‐vitamin K antagonist oral anticoagulants, LAAO was associated with no significant difference in the risk of the composite outcome and a lower risk of mortality, which suggests LAAO might be a reasonable option in select patients with atrial fibrillation. The observation of higher bleeding risk associated with LAAO highlights the need to optimize postprocedural antithrombotic regimens as well as systematic efforts to assess and address bleeding predispositions.

ObjectiveThis study was undertaken to assess cross‐sectional and longitudinal [18F]‐flortaucipir positron emission tomography (PET) uptake in pathologically confirmed frontotemporal lobar degeneration (FTLD) and to compare FTLD to cases with high and low levels of Alzheimer disease (AD) neuropathologic changes (ADNC).MethodsOne hundred forty‐three participants who had completed at least one flortaucipir PET and had autopsy‐confirmed FTLD (n = 52) or high (n = 58) or low ADNC (n = 33) based on Braak neurofibrillary tangle stages 0–IV versus V–VI were included. Flortaucipir standard uptake value ratios (SUVRs) were calculated for 9 regions of interest (ROIs): an FTLD meta‐ROI, midbrain, globus pallidum, an AD meta‐ROI, entorhinal, inferior temporal, orbitofrontal, precentral, and medial parietal. Linear mixed effects models were used to compare mean baseline SUVRs and annual rate of change in SUVR by group. Sensitivity and specificity to distinguish FTLD from high and low ADNC were calculated.ResultsBaseline uptake in the FTLD meta‐ROI, midbrain, and globus pallidus was greater in FTLD than high and low ADNC. No region showed a greater rate of flortaucipir accumulation in FTLD. Baseline uptake in the AD‐related regions and orbitofrontal and precentral cortices was greater in high ADNC, and all showed greater rates of accumulation compared to FTLD. Baseline differences were superior to longitudinal rates in differentiating FTLD from high and low ADNC. A simple baseline metric of midbrain/inferior temporal ratio of flortaucipir uptake provided good to excellent differentiation between FTLD and high and low ADNC (sensitivities/specificities = 94%/95% and 71%/70%).InterpretationThere are cross‐sectional and longitudinal differences in flortaucipir uptake between FTLD and high and low ADNC. However, optimum differentiation between FTLD and ADNC was achieved with baseline uptake rather than longitudinal rates. ANN NEUROL 2022;92:1016–1029

Background: The hippocampus and temporal lobe are atrophic in typical amnestic Alzheimer’s disease (tAD) and are used as imaging biomarkers in treatment trials. However, a better understanding of how temporal structures differ across atypical AD phenotypes and relate to cognition is needed. Objective: Our goal was to compare temporal lobe regions between tAD and two atypical AD phenotypes (logopenic progressive aphasia (LPA) and posterior cortical atrophy (PCA)), and assess cognitive associations. Methods: We age and gender-matched 77 tAD participants to 50 LPA and 27 PCA participants, all of which were amyloid-positive. We used linear mixed-effects models to compare FreeSurfer-derived hippocampal volumes and cortical thickness of entorhinal, inferior and middle temporal, and fusiform gyri, and to assess relationships between imaging and memory, naming, and visuospatial function across and within AD phenotype. Results: Hippocampal volume and entorhinal thickness were smaller bilaterally in tAD than LPA and PCA. PCA showed greater right inferior temporal and bilateral fusiform thinning and LPA showed greater left middle and inferior temporal and left fusiform thinning. Atypical AD phenotypes differed with greater right hemisphere thinning in PCA and greater left hemisphere thinning in LPA. Verbal and visual memory related most strongly to hippocampal volume; naming related to left temporal thickness; and visuospatial related to bilateral fusiform thickness. Fewer associations remained when examined within AD group. Conclusion: Atypical AD phenotypes are associated with greater thinning of lateral temporal structures, with relative sparing of medial temporal lobe, compared to tAD. These findings may have implications for future clinical trials in AD.