Lowe, Val J.

AbstractBackgroundWe previously demonstrated that Alzheimer’s disease (AD) patients ≥70 y/o tend to have smaller medial temporal volumes in the presence of relatively less medial temporal 18F‐flortaucipir‐PET uptake compared to young AD patients. TAR DNA‐binding protein 43 (TDP‐43) is frequently found in the brains of AD patients and is associated with old age and medial temporal atrophy. In this study, we aimed to determine whether TDP‐43 could be contributing to the mismatch between medial temporal volumes and flortaucipir‐PET uptake in older AD patients.MethodWe identified 77 participants (median age at death 79 years [range: 51‐99]) who had been enrolled into the Mayo Clinic Alzheimer’s Disease Research Center or Neurodegenerative Research Group (NRG), had undergone flortaucipir‐PET and volumetric MRI scans close to death, and autopsy confirmed AD neuropathological changes, as well as TDP‐43 assessments. Grey matter volumes and flortaucipir standard uptake value ratios (SUVRs) were calculated for hippocampus, amygdala, entorhinal, inferior temporal, and midfrontal cortices. We used bivariate‐response linear regression modelling, adjusting for Braak neurofibrillary tangle stage, to estimate the effect of TDP‐43 and age on gray matter volume and flortaucipir uptake, and tested whether TDP‐43 effect sizes varied according to age.ResultAfter accounting for Braak stage, older individuals tended to have significantly lower flortaucipir uptake across regions and lower hippocampal and entorhinal cortex (ERC) volumes. TDP‐43 was associated with reduced hippocampus and ERC volumes but was unrelated to flortaucipir SUVRs (Figure 1). Notably, the TDP‐43 effect size for the hippocampus and ERC was largely consistent across age, i.e., at any age, TDP‐43‐positive participants had significantly smaller hippocampal (∼12% decrease) and ERC (13% decrease) volumes than TDP‐43‐negative participants. However, at older ages, the mean volumes in the whole cohort and, more evidently, in those with dementia moved closer towards the mean of those with TDP‐43 (Figure 1), likely attributable to the increasing frequency of TDP‐43 with age.ConclusionTDP‐43 is a major contributor to the mismatch between flortaucipir uptake and medial temporal lobe volumes in older AD patients where lower medial temporal regional volumes are observed in the presence of less flortaucipir uptake.

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.

AbstractBackgroundStudies of aging and dementia often acquire brain MRI and PET images, then de‐identify and share these widely with other researchers. It is known that research participants can potentially be re‐identified from brain MRI and CT using face recognition, but this has not been tested with PET imaging.MethodWe generated face reconstruction images of 182 volunteers using their existing amyloid (PiB), tau (FTP), and FDG PET scans from clinical GE PET/CT scanners, and we measured how often commercial automatic face recognition (Microsoft Azure) correctly matched these reconstructions with photographs of their faces. We additionally compared these match rates with face reconstructions from CT (from PET/CT) and MRI. When testing face recognition from PET scans, CT was used only during PET reconstruction, not for face recognition. We also validated our updated mri_reface software, originally developed to de‐identify (“de‐face”) faces in MRI, with new support for PET images. We measured face recognition rates before and after applying mri_reface to PET, CT, and MRI. In addition, we measured effects of de‐facing on biomarker measurements (global and regional SUVR) using 244 ADNI Florbetapir (FBP) amyloid PET scans with two automated pipelines: FreeSurfer 6.0 (PETSurfer) and an in‐house SPM12‐based pipeline.ResultRates of successful face recognition with PET ranged from 32‐41% (Table 1) across tracers. These were lower than MRI (98%) and CT (78%) but still concerning for participant privacy. Using mri_reface reduced face recognition to 0‐3.5% for PET, 5% for CT, and 8% for MRI. mri_reface had little effect on global amyloid SUVR measurements from ADNI PET images (Figure 1). ICC values between original and de‐faced images were 1.00 with biases and median absolute differences < 0.5%, across both pipelines. Effects on individual regions all had ICC values > 0.98 with biases and median absolute differences < 2%.ConclusionFace reconstructions from PET images have sufficient quality for potential re‐identification using publicly available face recognition technology. Research studies should consider using face de‐identification (de‐facing) software on PET images in addition to CT and structural MRI. Our free, automatic mri_reface software greatly reduced the potential for face recognition without substantially affecting SUVR measurements.

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.

AbstractIntroductionAlzheimer's disease (AD) patients ≥70 years show smaller medial temporal volumes despite less 18F‐flortaucipir‐positron emission tomography (PET) uptake than younger counterparts. We investigated whether TAR DNA‐binding protein 43 (TDP‐43) was contributing to this volume‐uptake mismatch.MethodsSeventy‐seven participants with flortaucipir‐PET and volumetric magnetic resonance imaging underwent postmortem AD and TDP‐43 pathology assessments. Bivariate‐response linear regression estimated the effect of age and TDP‐43 pathology on volume and/or flortaucipir standardized uptake volume ratios of the hippocampus, amygdala, entorhinal, inferior temporal, and midfrontal cortices.ResultsOlder participants had lower hippocampal volumes and overall flortaucipir uptake. TDP‐43‐immunoreactivity correlated with reduced medial temporal volumes but was unrelated to flortaucipir uptake. TDP‐43 effect size was consistent across the age spectrum. However, at older ages, the cohort mean volumes moved toward those of TDP‐43‐positives, reflecting the increasing TDP‐43 pathology frequency with age.DiscussionTDP‐43 pathology is a relevant contributor driving the volume‐uptake mismatch in older AD participants.Highlights TDP‐43 pathology affects medial temporal volume loss but not tau radiotracer uptake. Greater TDP‐43 pathology effect is seen in old age due to its increasing frequency. TDP‐43 pathology is a relevant driver of the volume‐uptake mismatch in old AD patients.

Abstract BACKGROUND Interpretation of serial magnetic resonance imaging (MRI) for glioblastoma following radiation therapy (RT) is complicated by difficulty differentiating tumor from treatment-related changes, even using updated RANO criteria. The incorporation of novel imaging such as 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (18F-DOPA) PET/CT to post-treatment serial imaging may improve prognostication and better facilitate future treatment decisions. METHODS The secondary analysis of a recent phase II prospective clinical trial of 18F-DOPA PET/CT-directed dose-escalated RT included patients with IDH-wildtype, MGMT-unmethylated glioblastoma who underwent post-treatment serial 18F-DOPA PET/CT surveillance. Quantitative features were extracted from pre-RT and post-RT serial PET/CT images, and robust prognostic features were selected using an in-house workflow. Both an automated machine learning (ML) algorithm and an interpretable ML algorithm were utilized to correlate surveillance PET image features with subsequent survival of greater than 12 months versus less than 12 months from the surveillance timepoint. Changes from pre-RT to post-RT PET/CT (delta model) were also assessed for association with post-RT survival and validated with a separate cohort. RESULTS Thirty-five patients with IDH-wildtype, MGMT-unmethylated glioblastoma who had at least one available (range: 1-14) post-treatment 18F-DOPA PET/CT were included. Twenty-four were used for model training, while 11 were used for validation. Ultimately, a five-feature post-RT model utilizing two shape, two texture, and one first-order radiomic feature was selected. For the delta model, five texture, two shape, and one first order radiomic feature were selected. The models show 90% accuracy in predicting survival &lt; 12 months post-surveillance on the training set, and 68%-73% accuracy (AUC 0.64-0.73) for the validation cohort. Delta features were significantly associated with overall survival (p &lt; 0.05). CONCLUSIONS Post-RT serial 18F-DOPA PET/CT imaging can help distinguish true tumor progression in patients with glioblastoma using a radiomics model. Tumor response evaluated for changes from pre-RT to post-RT 18F-DOPA PET/CT also predicted subsequent overall survival.

AbstractGlycogen synthase kinase‐3 α/β is involved in dysregulation of neuronal tau protein in Alzheimer's disease (AD). There is an unmet clinical need for a blood‐brain barrier (BBB) permeable positron emission tomography (PET) probe for imaging of GSK‐3α/β in the brain to understand the pathogenesis of AD. Herein, we synthesized two PET probes, [18F]F‐CNBI and [18F]F‐CNPIFE, and evaluated their BBB permeability and affinity towards GSK‐3α/β. [19F]F‐CNPIFE showed higher in‐vitro binding towards GSK‐3α/β (IC50=19.4±2.5 nM; n=3, for GSK‐3α, IC50=19.4±3.8 nM; n=3, for GSK‐3β) compared to [19F]F‐CNBI (IC50=107.6±26.0 nM; n=4, for GSK‐3α, IC50=105.3±18.2 nM; n=3, for GSK‐3β). [18F]F‐CNPIFE showed 9.5‐fold higher brain uptake than [18F]F‐CNBI, in normal FVB/NJ mice, which was increased by additional 1.5‐fold on co‐administration of [19F]F‐CNPIFE with respect to [18F]F‐CNBI. Overall, [18F]F‐CNPIFE is a promising PET probe for GSK‐3α/β imaging and warrants further evaluation in an AD mouse model.