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Introduction FDG PET/CT was prospectively studied in 287 cN0 head and neck cancer patients in ACRIN 6685, and additional analysis of neck FDG uptake upon recurrence-free survival (RFS) and overall survival (OS) was performed. Patients and Methods Two hundred eight had analyzable data. Survival analysis was performed to compare RFS and OS based on neck FDG visual assessment (VA) and SUVmax. For SUVmax, the optimal thresholds were calculated using conditional inference trees on a randomly selected 70% training data set and validated using the remaining 30% of data. Kaplan-Meier curves with log-rank tests were generated for the patient groups based on VA and optimal SUVmax thresholds, and the hazards ratios (HRs) and 95% confidence intervals (CIs) were also calculated. Hypothesis testing was set at a significance level of 0.05. Results A total of 73.9% of bilateral cN0 and 50.0% of unilateral cN0 were alive at the end of the study with the remaining being dead or lost to follow-up. Overall survival median follow-up time was 24.0 months (interquartile range, 15.8–25.3; range, 0–37.0). A total of 63.3% of bilateral cN0 and 42.5% of unilateral cN0 patients remained disease free during the study. Recurrence-free survival median follow-up time was 23.9 months (interquartile range, 12.4–25.2; range, 0–35.6). Visual assessment of necks by our panel of radiologists was significantly associated with RFS (HR [95% CI], 2.30 [1.10–4.79]; P = 0.02), but not with OS (HR [95% CI], 1.64 [0.86–3.14]; P = 0.13). The optimal SUVmax thresholds were 2.5 for RFS and 5.0 for OS. For SUVmax assessment, applying the optimal thresholds to the 30% test data yielded HRs (95% CIs) of 2.09 (0.61–7.14; P = 0.23) for RFS and 3.42 (1.03–11.41; P = 0.03) for OS. The SUVmax threshold of 5.0 was significantly associated with RFS (HR [95% CI], 5.92 [1.79–19.57]; P < 0.001). Conclusions Neck FDG uptake by VA is significant for RFS. An SUVmax threshold of 5.0 is significantly associated with OS and RFS.

AbstractBackgroundAmyloidosis‐driven tau deposition has been a significant focus of imaging research in Alzheimer’s disease (AD). However, the 3R/4R tau seen in AD can be observed in the transentorhinal/entorhinal regions (Braak I/II) in the absence of amyloidosis. The underlying drivers of this phenomenon are not well understood. Our goal was to examine amyloid‐independent pathways of longitudinal Tau‐PET SUVR progression in the entorhinal cortex (ERC) using a broad range of risk and protective factors.MethodWe identified 449 participants from the population‐based sample of Mayo Clinic Study of Aging >50 years old with baseline amyloid PET and at least two serial tau PET scans (mean number of scans=2.3; mean follow‐up time=3.2 years). Using an amyloid SUVR cut‐point of 1.48, we categorized participants into A+ (n=167; mean age=77.5 years; 56% male; baseline ERC tau=1.20; 83% cognitively unimpaired) and A‐ (n=282; mean age=67.5; 56% male; baseline ERC tau=1.07; 92% cognitively unimpaired). We used an ERC tau SUVR cut‐point of 1.21 to determine T+. We fit mixed effects and growth curve models with age, sex, baseline amyloid, risk factors (midlife activities, sleep, stress, education/occupation, cardiovascular metabolic conditions) as predictors and longitudinal ERC tau as the outcome. We fit separate models in A‐, A+, and all participants.ResultsFor models of all participants and A+ participants, the only predictors of higher rates of ERC tau deposition were increasing age and amyloid SUVR (p<0.05). In the A‐ models (figure below), amyloid SUVR and age were predictors of baseline, but not longitudinal ERC tau. However, a greater number of cardiovascular metabolic conditions predicted a higher rate of ERC tau accumulation (0.007 (0.002), p<0.05). Only 13% of the 23 A‐T+ participants were APOE4 carriers (compared to 31% in the overall sample), suggesting the role of non‐APOE4 pathways to tau deposition.ConclusionOur results support previous evidence that significant amyloidosis is causal to higher rates of tau deposition. However, in those without significant amyloidosis, cardiovascular metabolic conditions predicted higher rates of ERC tau accumulation. Further work is warranted to investigate the mechanisms through which genetics, comorbidities, and lifestyle may play a role in early tau deposition.

AbstractBackgroundβ‐amyloid plaques, a pathological hallmark of Alzheimer’s disease, are common in dementia with Lewy bodies (DLB). However, little is known about when β‐amyloid levels increase throughout the Lewy body disease (LBD) continuum, from an early prodromal stage of isolated REM sleep behavior disorder (iRBD), to a stage of mild cognitive impairment‐Lewy bodies (MCI‐LB), and finally DLB. Our objective was to investigate β‐amyloid deposition throughout the LBD continuum, as well as the influence of sex and APOE ε4 status, as potential factors contributing to Alzheimer’s disease pathological changes.MethodPatients with iRBD (n=24), MCI‐LB (n=62), and DLB (n=82) from the Mayo Clinic Alzheimer’s Disease Research Center and Center for Sleep Medicine were included. β‐amyloid levels were measured by Pittsburgh compound B (PiB) PET and global cortical standard uptake value ratio (SUVR) was calculated. Global cortical PiB SUVR values from each clinical group were compared to each other and to cognitively unimpaired individuals (CU; n=100) balanced on age and sex from the Mayo Clinic Study of Aging (MCSA). Clinical groups were compared using ANCOVA after adjusting by age.ResultMCI‐LB and DLB patients had significantly higher global cortical PiB SUVR than CU participants, while PiB SUVR in iRBD patients was comparable to that of CU participants (Figure 1). DLB patients also had significantly higher global cortical PiB SUVR compared to iRBD patients (p=0.004). In MCI‐LB and DLB, global cortical PiB SUVR was higher in APOE ε4 carriers compared to APOE ε4 non‐carriers (p<0.001), and women had higher global cortical PiB SUVR compared to men (p=0.024).ConclusionIn this cross‐sectional study, β‐amyloid pathology gradually increased throughout the clinically defined LBD continuum. Whereas β‐amyloid levels are comparable to cognitively unimpaired individuals in iRBD, a significant elevation in β‐amyloid levels coincides with cognitive impairment observed in MCI‐LB and DLB. Specifically, women and APOE ε4 carriers tend to have higher β‐amyloid levels than men and APOE ε4 non‐carriers. These findings have important implications for targeting patients within the LBD continuum, who may benefit from amyloid modifying therapies.

AbstractBackgroundPosterior Cortical Atrophy (PCA) is one of the atypical Alzheimer’s Disease (AD) variants, characterized by predominant visuospatial and visuoperceptual deficits, with dorsal and ventral subtypes recognized clinically and radiographically. A third primary occipital (caudal) variant has been suggested. The objective of this study was to determine its demographics, clinical manifestations, and biomarker findings.MethodFifty‐two PCA patients with positive amyloid PET scans were investigated. Patients underwent neuropsychological assessment, 3T MRI imaging and FDG‐, amyloid‐ and tau‐PET scans. Regional FDG‐PET values were normalized to the pons and represented as z‐scores relative to a control population. Patients were divided into “primary occipital” and “other PCA” subgroups according to FDG‐PET defined criteria, with primary occipital defined as patients in which the z‐scores for occipital subregions were at least one standard deviation lower (i.e., more abnormal) than the z scores in all other brain regions. Global amyloid‐PET, temporoparietal FDG‐PET and temporal tau‐PET regions‐of‐interest (ROI) were calculated. Student’s T test and Fisher’s exact analyses were used to investigate differences in demographics, clinical findings, and imaging biomarkers across the two groups.ResultNine patients were classified as primary occipital; primary occipital patients were older (64.8±6.9 versus 59.3±6.8, p=0.034) and had more years of education (17.7±1.7 versus 15.1±2.6, p=0.007) compared to the other PCA patients. Clinical evaluation revealed worse performance for the primary occipital group on the Ishihara test for color perception (p<0.001), while the other PCA patients performed worse on the Western Aphasia Battery (WAB) praxis scale (p=0.005). Overall neuropsychiatric symptom burden as defined by the NPI‐Q was lower in the primary occipital group (p<0.001). The FDG‐PET meta‐ROI (weighted median uptake in angular gyrus, posterior cingulate, and inferior temporal ROIs, normalized to the pons and vermis) was higher in the primary occipital subtype (p=0.006), but no differences were observed in amyloid and tau‐PET.ConclusionClinical and biomarker findings suggest primary occipital PCA is characterized by an older age at onset, more color perception dysfunction, less severe ideomotor apraxia and less hypometabolism in temporal‐parietal meta‐ROI compared to more established phenotypes.

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.

AbstractBackgroundPosterior cortical atrophy (PCA) is associated with hypometabolism on FDG‐PET and atrophy on MRI in the parietal and occipital lobes, and patients often show the cingulate island sign (CIS) whereby posterior cingulate metabolism is relatively spared compared to the precuneus and cuneus. It has been hypothesized that medial temporal sparing may be related to CIS in PCA. We aimed to assess the severity of medial temporal atrophy in PCA and determine whether the presence of a CIS is related to medial temporal sparing.MethodFifty‐five PCA patients underwent a 3T volumetric MRI and FDG‐PET. The degree and symmetry of medial temporal atrophy on MRI was visually assessed in both hemispheres using the five‐point Scheltens scale (0=normal; 1=mild; 2=moderate; 3=marked; 4=severe). Visual assessments of FDG‐PET coded the presence/absence of a CIS and whether the CIS was symmetric or asymmetric. Hippocampal volumes and a quantitative CIS were also measured. Associations between medial temporal atrophy scores and clinical measures were assessed using linear regression correcting for age.ResultMedial temporal atrophy was most commonly mild (38% on the right, 40% on the left), followed by moderate (35% on the right, 29% on the left), and then no atrophy (18% on right, 25% on left). Marked or severe atrophy was only observed in 9% of patients on the right and 5% of patients on the left. Atrophy was asymmetric in 55%, and when asymmetric was most commonly worse on the right (76%). Greater medial temporal atrophy was associated with older age (p<0.001) and worse verbal episodic memory (p=0.008). The CIS was observed in 44% of the PCA patients and was asymmetric in 50% of these. The patients with a CIS showed greater medial temporal asymmetry (p=0.016), but did not show lower medial temporal atrophy scores, compared to those without a CIS. Hippocampal volumes were not associated with quantitative CIS.ConclusionMild to moderate medial temporal atrophy is a common finding in PCA and is associated with worse memory impairment and older age. However, medial temporal sparing was not related to the presence of a CIS in PCA.

AbstractBackgroundThe dynamics of white matter (WM) changes are understudied in Alzheimer’s disease (AD). Leveraging tau deposition measured from Tau‐PET, our goal was to study the association between tau and white matter health using NODDI from multi‐shell diffusion MRI (dMRI). Specifically, we focused on the Neurite Density Index (NDI) from NODDI, which in WM captures the signal from axons.MethodWe identified 251 participants (126 cognitively normal, 58 MCI, 67 dementia) from the Mayo ADRC and Mayo Clinic Study of Aging with Aβ‐PET, Tau‐PET, and multi‐shell dMRI who were Aβ‐positive (mean age of 77 years and 55% male). We limited to Aβ‐positive participants because Aβ deposition accelerates tau deposition. For Tau‐PET, we estimated SUVRs from three regions corresponding to Braak I/II, III/IV, and V/VI (Figure 1.a). To understand the associations between Tau‐PET SUVRs at three Braak regions and NDIs at 30 candidate WM tracts, we implemented three analyses methods ‐ Pearson correlation, linear regression, and multilayer perceptron machine learning regression.ResultHigher tau burden in all three Braak regions was associated with lower neurite density in 10 significant WM tracts (Table 1). Interestingly, uncinate fasciculus (UNC) was found significantly associated with all three Braak regions. All three analysis methods consistently showed differential involvement of WM pathways for Braak staging such as ‐ Cingulum adjoining Hippocampus (CGH) for Braak I/II regions, Medial Temporal WM (MTWM) for Braak III/IV regions, and Inferior Occipital WM (IOWM) for Braak V/VI regions (Figure 1.b). The linear regression and machine learning regression models also suggested that WM measurements reflected 18‐35% of variance due to tau depositionConclusionWe found that CGH and UNC WM were the most impacted ROIs associated with Braak I/II regions as expected. UNC, directly connecting anterior temporal lobes to the orbitofrontal cortex, was a key WM tract identified as important for all Braak I‐VI regions and warrants further investigation in the study of AD progression. This study highlights the significant impact tau deposition may have on WM. This work also demonstrates the feasibility of using NODDI as an informative supplementary imaging modality for monitoring AD progression.

AbstractBackgroundReduced striatal uptake on ioflupane SPECT is considered an indicative biomarker for Dementia with Lewy Bodies (DLB), and has been proposed as an indicative biomarker for Mild Cognitive Impairment with Lewy Bodies (MCI‐LB). Little data exists on the profile of uptake on key striatal regions of interest (ROIs) in DLB or MCI‐LB.MethodParticipants with DLB or MCI‐LB at baseline based on the presence of ≥2 core clinical DLB features, and ≥2 ioflupane SPECT scans performed approximately annually, were identified from the Mayo ADRC database. Minimum brain laterality z‐score values for the putamen (whole, anterior and posterior), caudate and striatum were determined using DaTQUANT 2 software. Baseline clinical and scan data was used for all analyses, and the annualized minimum putamen z‐change was determined on serial scans. The scan was considered abnormal if the minimum putamen z‐score was <‐0.98 based on clinicopathologic data.Result27 participants were included (21 DLB and 6 MCI‐LB, 96% male). Compared to MCI‐LB, DLB had a lower mean MoCA score and a trend toward a higher UPDRS score compared to MCI‐LB; there were no statistically significant differences in age, sex, onset age or duration of cognitive decline, or presence of other core DLB features. The majority (89%) had an abnormal scan at baseline. All ROI z‐scores showed the expected general trend of DLB<MCI‐LB, and the annualized z‐change was more negative in DLB, but none of the findings reached statistical significance.ConclusionThese findings suggest in MCI‐LB and DLB that 1) an abnormal ioflupane SPECT scan is frequently present at diagnosis, 2) z‐scores become more negative over time, and 3) the annualized z‐change is similar. Similar findings in larger cohorts would support the use of ioflupane SPECT as a biomarker in both MCI‐LB and DLB clinical trials.