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BackgroundIndividuals with Down syndrome (DS) are at high risk of early-onset Alzheimer's disease (AD); yet, some 20 percent do not develop any signs of dementia until after 65 years or in their lifetime. Mosaicism could contribute to this phenotypic variation, where some disomic cells could lead to lower levels of gene products from chromosome 21.MethodsWe examined longitudinal neuropsychological and biomarker data from two large studies of DS: the Alzheimer Biomarker Consortium-Down syndrome study (ABC-DS) (n = 357); and a legacy study (n = 468). We assessed mosaicism using karyotyping or GWAS data. Participants had data on plasma AD biomarkers (Aβ40, Aβ42, tau, and NfL) and longitudinal cognitive measures. A subset had cerebrospinal fluid biomarkers (Aβ40, Aβ42, tau, ptau181, and NfL) and amyloid and tau PET data.FindingsFor both cohorts, the prevalence of mosaicism was

By age 40 years, over 90% of adults with Down syndrome have Alzheimer's disease pathology and most progress to dementia. Despite having few systemic vascular risk factors, individuals with Down syndrome have elevated cerebrovascular disease markers that track with the clinical progression of Alzheimer's disease, suggesting a role of cerebrovascular disease that is hypothesized to be mediated by inflammatory factors. This study examined the pathways through which small vessel cerebrovascular disease contributes to Alzheimer's disease-related pathophysiology and neurodegeneration in adults with Down syndrome. One hundred eighty-five participants from the Alzheimer's Biomarkers Consortium-Down Syndrome [mean (SD) age = 45.2 (9.3) years] with available MRI and plasma biomarker data were included in this study. White matter hyperintensity (WMH) volumes were derived from T2-weighted fluid-attenuated inversion recovery MRI scans, and plasma biomarker concentrations of amyloid beta 42/40, phosphorylated tau 217, astrocytosis (glial fibrillary acidic protein) and neurodegeneration (neurofilament light chain) were measured with ultrasensitive immunoassays. We examined the bivariate relationships of WMH, amyloid beta 42/40, phosphorylated tau 217 and glial fibrillary acidic protein with age-residualized neurofilament light chain across Alzheimer's disease diagnostic groups. A series of mediation and path analyses examined statistical pathways linking WMH and Alzheimer's disease pathophysiology to promote neurodegeneration in the total sample and groups stratified by clinical diagnosis. There was a direct and indirect bidirectional effect through the glial fibrillary acidic protein of WMH on phosphorylated tau 217 concentration, which was associated with neurofilament light chain concentration in the entire sample. Amongst cognitively stable participants, WMH was directly and indirectly, through glial fibrillary acidic protein, associated with phosphorylated tau 217 concentration, and in those with mild cognitive impairment, there was a direct effect of WMH on phosphorylated tau 217 and neurofilament light chain concentrations. There were no associations of WMH with biomarker concentrations among those diagnosed with dementia. The findings from this cross-sectional study suggest that among individuals with Down syndrome, cerebrovascular disease promotes neurodegeneration by increasing astrocytosis and tau pathophysiology in the presymptomatic phases of Alzheimer's disease, but future studies will need to confirm these associations with longitudinal data. This work joins an emerging literature that implicates cerebrovascular disease and its interface with neuroinflammation as a core pathological feature of Alzheimer's disease in adults with Down syndrome.

BackgroundImportant insights into the early pathogenesis of Alzheimer's disease can be provided by studies of autosomal dominant Alzheimer's disease and Down syndrome. However, it is unclear whether the timing and spatial distribution of amyloid accumulation differs between people with autosomal dominant Alzheimer's disease and those with Down syndrome. We aimed to directly compare amyloid changes between these two groups of people.MethodsIn this cross-sectional study, we included participants (aged ≥25 years) with Down syndrome and sibling controls who had MRI and amyloid PET scans in the first data release (January, 2020) of the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS) study. We also included carriers of autosomal dominant Alzheimer's disease genetic mutations and non-carrier familial controls who were within a similar age range to ABC-DS participants (25-73 years) and had MRI and amyloid PET scans at the time of a data freeze (December, 2020) of the Dominantly Inherited Alzheimer Network (DIAN) study. Controls from the two studies were combined into a single group. All DIAN study participants had genetic testing to determine PSEN1, PSEN2, or APP mutation status. APOE genotype was determined from blood samples. CSF samples were collected in a subset of ABC-DS and DIAN participants and the ratio of amyloid β42 (Aβ42) to Aβ40 (Aβ42/40) was measured to evaluate its Spearman's correlation with amyloid PET. Global PET amyloid burden was compared with regards to cognitive status, APOE ɛ4 status, sex, age, and estimated years to symptom onset. We further analysed amyloid PET deposition by autosomal dominant mutation type. We also assessed regional patterns of amyloid accumulation by estimated number of years to symptom onset. Within a subset of participants the relationship between amyloid PET and CSF Aβ42/40 was evaluated.Findings192 individuals with Down syndrome and 33 sibling controls from the ABC-DS study and 265 carriers of autosomal dominant Alzheimer's disease mutations and 169 non-carrier familial controls from the DIAN study were included in our analyses. PET amyloid centiloid and CSF Aβ42/40 were negatively correlated in carriers of autosomal dominant Alzheimer's disease mutations (n=216; r=-0·565; p

Research suggests a link between Alzheimer's Disease in Down Syndrome (DS) and the overproduction of amyloid plaques. Using Positron Emission Tomography (PET) we can assess the in-vivo regional amyloid load using several available ligands. To measure amyloid distributions in specific brain regions, a brain atlas is used. A popular method of creating a brain atlas is to segment a participant's structural Magnetic Resonance Imaging (MRI) scan. Acquiring an MRI is often challenging in intellectually-imparied populations because of contraindications or data exclusion due to significant motion artifacts or incomplete sequences related to general discomfort. When an MRI cannot be acquired, it is typically replaced with a standardized brain atlas derived from neurotypical populations (i.e. healthy individuals without DS) which may be inappropriate for use in DS. In this project, we create a series of disease and diagnosis-specific (cognitively stable (CS-DS), mild cognitive impairment (MCI-DS), and dementia (DEM-DS)) probabilistic group atlases of participants with DS and evaluate their accuracy of quantifying regional amyloid load compared to the individually-based MRI segmentations. Further, we compare the diagnostic-specific atlases with a probabilistic atlas constructed from similar-aged cognitively-stable neurotypical participants. We hypothesized that regional PET signals will best match the individually-based MRI segmentations by using DS group atlases that aligns with a participant's disorder and disease status (e.g. DS and MCI-DS). Our results vary by brain region but generally show that using a disorder-specific atlas in DS better matches the individually-based MRI segmentations than using an atlas constructed from cognitively-stable neurotypical participants. We found no additional benefit of using diagnose-specific atlases matching disease status. All atlases are made publicly available for the research community.HighlightDown syndrome (DS) joint-label-fusion atlases provide accurate positron emission tomography (PET) amyloid measurements.A disorder-specific DS atlas is better than a neurotypical atlas for PET quantification.It is not necessary to use a disease-state-specific atlas for quantification in aged DS.Dorsal striatum results vary, possibly due to this region and dementia progression.

BackgroundOfranergene obadenovec (VB-111) is an anticancer viral therapy that demonstrated in a phase II study a survival benefit for patients with recurrent glioblastoma (rGBM) who were primed with VB-111 monotherapy that was continued after progression with concomitant bevacizumab.MethodsThis pivotal phase III randomized, controlled trial compared the efficacy and safety of upfront combination of VB-111 and bevacizumab versus bevacizumab monotherapy. Patients were randomized 1:1 to receive VB-111 1013 viral particles every 8 weeks in combination with bevacizumab 10 mg/kg every 2 weeks (combination arm) or bevacizumab monotherapy (control arm). The primary endpoint was overall survival (OS), and secondary endpoints were objective response rate (ORR) by Response Assessment in Neuro-Oncology (RANO) criteria and progression-free survival (PFS).ResultsEnrolled were 256 patients at 57 sites. Median exposure to VB-111 was 4 months. The study did not meet its primary or secondary goals. Median OS was 6.8 versus 7.9 months in the combination versus control arm (hazard ratio, 1.20; 95% CI: 0.91-1.59; P = 0.19) and ORR was 27.3% versus 21.9% (P = 0.26). A higher rate of grades 3-5 adverse events was reported in the combination arm (67% vs 40%), mainly attributed to a higher rate of CNS and flu-like/fever events. Trends for improved survival with combination treatment were seen in the subgroup of patients with smaller tumors and in patients who had a posttreatment febrile reaction.ConclusionsIn this study, upfront concomitant administration of VB-111 and bevacizumab failed to improve outcomes in rGBM. Change of treatment regimen, with the lack of VB-111 monotherapy priming, may explain the differences from the favorable phase II results.Clinical trials registrationNCT02511405.

IntroductionDown syndrome (DS) is associated with a higher risk of dementia. We hypothesize that amyloid beta (Aβ) in specific brain regions differentiates mild cognitive impairment in DS (MCI-DS) and test these hypotheses using cross-sectional and longitudinal data.Methods18F-AV-45 (florbetapir) positron emission tomography (PET) data were collected to analyze amyloid burden in 58 participants clinically classified as cognitively stable (CS) or MCI-DS and 12 longitudinal CS participants.ResultsThe study confirmed our hypotheses of increased amyloid in inferior parietal, lateral occipital, and superior frontal regions as the main effects differentiating MCI-DS from the CS groups. The largest annualized amyloid increases in longitudinal CS data were in the rostral middle frontal, superior frontal, superior/middle temporal, and posterior cingulate cortices.DiscussionThis study helps us to understand amyloid in the MCI-DS transitional state between cognitively stable aging and frank dementia in DS. The spatial distribution of Aβ may be a reliable indicator of MCI-DS in DS.

Purpose: The National Institute of Health Toolbox Cognition Battery (NIHTB-CB) is increasingly used in Alzheimer disease (AD) research. We examined the relation of AD biomarkers with performance in the NIHTB-CB in late middle age. Methods: This is a cross-sectional analysis of 334 Hispanic participants aged 64.22±3.35 years from a study of AD biomarkers. White matter hyperintensities (WMH), infarcts, and cortical thickness in AD regions (CT) were assessed with 3T magnetic resonance imaging. Amyloid and tau were assessed with 18F-Florbetaben and 18F-MK6240 positron emission tomography, respectively. Results: Lower CT and infarcts were associated with worse Oral Reading Recognition and Cognition Crystallized Composite scores. Lower CT and higher WMH were associated with worse Pattern Comparison Processing Speed. Amyloid and tau were not associated with any test. Discussion: Amyloid and tau, the culprits of AD, are not related to the NIHTB-CB in late middle age. Continued follow-up will reveal if AD impacts performance on the NIHTB-CB. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Evidence suggests microglial activation precedes regional tau and neurodegeneration in Alzheimer's disease (AD). We characterized microglia with translocator protein (TSPO) positron emission tomography (PET) within an AD progression model where global amyloid beta (Aβ) precedes local tau and neurodegeneration, resulting in cognitive impairment. METHODS: Florbetaben, PBR28, and MK‐6240 PET, T1 magnetic resonance imaging, and cognitive measures were performed in 19 cognitively unimpaired older adults and 22 patients with mild cognitive impairment or mild AD to examine associations among microglia activation, Aβ, tau, and cognition, adjusting for neurodegeneration. Mediation analyses evaluated the possible role of microglial activation along the AD progression model. RESULTS: Higher PBR28 uptake was associated with higher Aβ, higher tau, and lower MMSE score, independent of neurodegeneration. PBR28 mediated associations between tau in early and middle Braak stages, between tau and neurodegeneration, and between neurodegeneration and cognition. DISCUSSION: Microglia are associated with AD pathology and cognition and may mediate relationships between subsequent steps in AD progression. [ABSTRACT FROM AUTHOR]

Background: Psychosis is a debilitating cluster of symptoms occurring in some Alzheimer's Disease (AD) patients, and is defined by the presence of delusions and/or hallucinations. Previous studies suggest that psychosis in AD patients is associated with increased tau burden and accelerated cognitive decline. We sought to assess differences in tau burden between cognitively impaired patients with psychosis(+P) and those without psychosis(‐P). Method: Twenty‐six ADNI participants with psychosis (+P; 11.5% CDR0, 42.3% CDR0.5, 30.7% CDR1, 11.5% CDR2, 4% CDR3, 77±7yr old, 50% women, 16±3yr education, 92% White) and 26 without psychosis were matched by CDR, age, sex, education, and race (‐P; 11.5% CDR0, 42.3% CDR0.5, 30.7% CDR1, 11.5% CDR2, 4% CDR3, 77±6yr old, 54% women, 15±3yr education, 92% White). The +P group was defined based on any endorsement of hallucinations or delusions on Neuropsychiatric Inventory. Flortaucipir PET data were processed according to ADNI methods. Difference in tau burden was assessed using a paired t‐test on the ROI and voxel‐level between +P and ‐P groups in Braak regions as well as the amygdala, hippocampus, and frontal, temporal, and parietal lobes A follow‐up voxelwise t‐test was performed within the +P group between those with and without concurrent symptoms at the time of their scan. Analyses were corrected for amyloid‐level and voxel‐wise analyses were evaluated at p<0.001 without multiple comparisons correction. Result: Descriptively, participant‐level scans demonstrated a variety of tau patterns, often with right laterality. Tau was elevated for +P participants compared to ‐P in the amygdala (0.6 [0.25, 0.96]; p = 0.003) and the hippocampus (0.3 [0.04, 0.55]; p = 0.03), while marginally elevated in the frontal cortex (0.09 [‐0.002, 0.189]; p = 0.06) [Figure1]. Voxel‐wise, elevated tau was localized to the amygdala, hippocampus, thalamus, and right frontal lobe [Figure2]. Additionally, with concurrent delusion symptomatology, elevated tau was localized to Braak III/IV regions [Figure3]. Conclusion: Tau burden in the amygdala may be a potential biomarker of psychosis across the AD continuum. Right‐sided laterality is consistent with psychosis‐related structural changes and psychosis‐related tau binding needs to be assessed further. Given that early psychosis in AD is associated with faster cognitive decline, these results could inform the development of prognostic biomarkers. [ABSTRACT FROM AUTHOR]

Background: Noninvasive yet accurate PET quantification methods are crucial for safe yet meaningful PET studies. [11C]ER176 is a PET radioligand that images neuroinflammation via the 18‐kDa translocator protein (TSPO), which is upregulated in activated microglia and has previously been a PET target in Alzheimer's disease research. Currently, the gold‐standard method for quantifying ER176 PET is kinetic modeling of the dynamic PET data using the arterial input function (AIF), which is invasive and expensive. The current study explores a pseudo‐reference region (RR) technique to noninvasively quantify ER176 scans to facilitate clinical PET studies. Method: Dynamic 90‐min [11C]ER176 PET scans were acquired in 11 participants, 5 controls (5M/0F) and 6 patients (3 MCI‐3M/0F; 3 AD‐2M/1F). Arterial blood data were collected throughout the duration of the scan and were used along with the dynamic imaging data to determine regional total volume of distribution (VT) using the two‐tissue compartment model. Standardized Uptake Value Ratio (SUVR) was calculated by normalizing the radioactivity in each region by that of the reference region (cerebellar gray matter). We explored differences across methods (AIF‐derived VT vs. RR‐derived SUVR) and across groups (controls vs. patients). Result: Within each group, the outcome measures from AIF and RR methods were well correlated (Controls: R2 = 0.986, p < 0.001; Patients: R2 = 0.983, p < 0.001; Figure 1). Both methods showed greater TSPO expression in patients compared to controls, with mean differences in the medial temporal cortex of 14.99% between groups for VT and 10.65% between groups for SUVR, but lower variance for SUVR compared to VT (Figure 2). Both quantification methods showed similar regional patterns of percent differences between groups, corresponding to tau progression along Braak staging (Figure 3). Conclusion: Results suggest that using the cerebellum, which does not accumulate amyloid plaques or tau aggregates until end‐stage disease, as a reference region for [11C]ER176 may be a valid method for quantifying differences between AD patients and controls compared to gold‐standard methodology. Regional increases in neuroinflammation are more reliable for SUVR than VT in AD‐related regions. Ongoing work includes a larger sample size to further validate noninvasive PET quantification. [ABSTRACT FROM AUTHOR]

Background: 18F‐MK6240 binds hyperphosphorylated tau protein in neurofibrillary tangles, a hallmark of Alzheimer's disease(AD); however, the tracer may also bind to other similarly structured molecules, known as off‐target binding. Off‐target binding is inconsistent across individuals and can lead to biased quantitative assessment, making it difficult to accurately identify neurofibrillary tangle pathology. We examined variable off‐target binding by performing a correlation analysis with demographic factors, vascular risk, lifestyle factors, and co‐morbidities. Methods: Ninety‐five participants (65±3yrs; 67% women; 42/58% White/Black; 66% Hispanic; 12±4yrs education) in the Northern Manhattan Study of Metabolism and Mind(NOMEM) underwent 18F‐MK6240(90‐110min) and 18F‐Florbetaben(90‐110min) PET imaging and completed surveys, including age, sex, education, exercise levels, and medical history. Only visually‐determined amyloid‐negative participants were included to increase the likelihood that elevated tau PET signal was off‐target. Tau SUVR (cerebellar gray matter reference region) was calculated for composite Braak stages (I/II, III/IV, V/VI) and two known off‐target regions, the choroid plexus and the extra‐cerebral space around the brain. The extra‐cerebral mask was generated by dilating the gray/CSF boundary of the ANTs probability map and then subtracting the brain. Percent of variance explained (r2) from general linear models are reported between off‐target regions and factors of interest. Results: Descriptively, 18F‐MK6240 off‐target binding in choroid plexus was positively correlated with SUVR in Braak stage regions I/II (r2 = 0.37;p<0.001) and III/IV (r2 = 0.44;p<0.001) to a larger extent than V/VI (r2 = 0.21;p<0.001), while off‐target binding in the extra‐cerebral space was positively correlated with Braak stages I/II (r2 = 0.07;p<0.001) to a lesser extent than III/IV (r2 = 0.13;p<0.001) and V/VI (r2 = 0.18;p<0.001), suggesting that proximity of key AD regions to off‐target regions increases the likelihood of confounded signal. Off‐target binding in the choroid plexus was elevated amongst self‐identified Hispanic participants (r2 = 0.19;p<0.001). Extra‐cerebral off‐target binding was negatively associated with participant weight (r2 = 0.12;p<0.001). Conclusion: Extra‐cerebral off‐target binding of 18F‐MK6240 may confound signal in key AD regions in amyloid‐negative participants. Off‐target binding should be accounted for regionally and methods for capturing extra‐cerebral signal are critical. Understanding the role of factors related to tracer distribution, including weight, and sociodemographic factors will enhance the quantification of 18F‐MK6240 for the early detection of neurofibrillary tangle pathology. [ABSTRACT FROM AUTHOR]

Background: Noninvasive yet accurate PET quantification methods are crucial for safe yet meaningful PET studies. [11C]ER176 is a PET radioligand that images neuroinflammation via the 18‐kDa translocator protein (TSPO), which is upregulated in activated microglia and has previously been a PET target in Alzheimer's disease research. Currently, the gold‐standard method for quantifying ER176 PET is kinetic modeling of the dynamic PET data using the arterial input function (AIF), which is invasive and expensive. The current study explores a pseudo‐reference region (RR) technique to noninvasively quantify ER176 scans to facilitate clinical PET studies. Method: Dynamic 90‐min [11C]ER176 PET scans were acquired in 11 participants, 5 controls (5M/0F) and 6 patients (3 MCI‐3M/0F; 3 AD‐2M/1F). Arterial blood data were collected throughout the duration of the scan and were used along with the dynamic imaging data to determine regional total volume of distribution (VT) using the two‐tissue compartment model. Standardized Uptake Value Ratio (SUVR) was calculated by normalizing the radioactivity in each region by that of the reference region (cerebellar gray matter). We explored differences across methods (AIF‐derived VT vs. RR‐derived SUVR) and across groups (controls vs. patients). Result: Within each group, the outcome measures from AIF and RR methods were well correlated (Controls: R2 = 0.986, p < 0.001; Patients: R2 = 0.983, p < 0.001; Figure 1). Both methods showed greater TSPO expression in patients compared to controls, with mean differences in the medial temporal cortex of 14.99% between groups for VT and 10.65% between groups for SUVR, but lower variance for SUVR compared to VT (Figure 2). Both quantification methods showed similar regional patterns of percent differences between groups, corresponding to tau progression along Braak staging (Figure 3). Conclusion: Results suggest that using the cerebellum, which does not accumulate amyloid plaques or tau aggregates until end‐stage disease, as a reference region for [11C]ER176 may be a valid method for quantifying differences between AD patients and controls compared to gold‐standard methodology. Regional increases in neuroinflammation are more reliable for SUVR than VT in AD‐related regions. Ongoing work includes a larger sample size to further validate noninvasive PET quantification. [ABSTRACT FROM AUTHOR]

Introduction: Positron emission tomography (PET) imaging for neurofibrillary tau allows investigation of the in vivo spatiotemporal progression of Alzheimer's disease (AD) pathology. We evaluated the suitability of 18F‐MK‐6240 in a clinical sample and determined the relationships among 18F‐MK‐6240 binding, age, cognition, and cerebrospinal fluid (CSF)‐based AD biomarkers. Methods: Participants (n = 101, 72 ± 9 years, 52% women) underwent amyloid PET, tau PET, structural T1‐weighted magnetic resonance imaging, and neuropsychological evaluation. Twenty‐one participants had lumbar puncture for CSF measurement of amyloid beta (Aβ)42, tau, and phosphorylated tau (p‐tau). Results: 18F‐MK‐6240 recapitulated Braak staging and correlated with CSF tau and p‐tau, normalized to Aβ42. 18F‐MK‐6240 negatively correlated with age across Braak regions in amyloid‐positive participants, consistent with greater tau pathology in earlier onset AD. Domain‐specific, regional patterns of 18F‐MK‐6240 binding were associated with reduced memory, executive, and language performance, but only in amyloid‐positive participants. Discussion: 18F‐MK‐6240 can approximate Braak staging across the AD continuum and provide region‐dependent insights into biomarker‐based AD models. [ABSTRACT FROM AUTHOR]

Introduction: Odor identification deficits characterize Alzheimer's disease and other dementias. We examined if intact performance on brief cognitive and odor identification tests predicts lack of transition to dementia. Methods: In an urban community, 1037 older adults without dementia completed the 40‐item University of Pennsylvania Smell Identification Test, which includes the 12‐item Brief Smell Identification Test (B‐SIT). Data from 749 participants followed up for 4 years were analyzed. Results: In covariate‐adjusted survival analyses, impairment on the Blessed Orientation Memory Concentration Test and B‐SIT each predicted dementia (n = 109), primarily Alzheimer's disease (n = 101). Among participants with intact olfactory (B‐SIT ≥ 11/12 correct) and cognitive (Blessed Orientation Memory Concentration Test ≤ 5/28 incorrect) ability, 3.4% (4/117) transitioned to dementia during follow‐up with no transitions in the 70‐75 and 81‐83 years age group quartiles. Discussion: Odor identification testing adds value to global cognitive testing, and together can identify individuals who rarely transition to dementia, thereby avoiding unnecessary diagnostic investigation. [ABSTRACT FROM AUTHOR]

Background: Neural networks are increasingly being applied towards neuroimaging problems in AD, but performance is often limited by lack of adequate numbers of samples. This is commonly alleviated by pre‐training the model on a related task beforehand (i.e. 'transfer learning'). To our knowledge, there exists no set/standard of widely available pretrained model weights for 3D medical images. Tau PET radiotracers such as AV‐1451 and MK‐6240 have highly similar binding patterns, and so may be well suited to transfer learning tasks. Method: Subjects with AV‐1451 scans (n=446) were downloaded from ADNI, while subjects with MK‐6240 scans (n= 320) were pooled from multiple studies recruiting at CUIMC. We trained multiple 3D neural networks (all with the same modified InceptionV3 architecture) to differentiate patients (i.e. MCI/AD) from controls based on their tau PET scan, both with and without pretraining information from prior training iterations of our models. We compare performance on holdout sets for each validation fold (n= 64 for MK‐6240 and n=89 for AV‐1451) using DeLong's test for ROC curves and analyze features learned by models with t‐SNE plots. Result: The AV‐1451 Model pretrained on MK‐6240 scans had superior performance to the AV‐1451 model not using pretraining (AUC = 0.74 vs. 0.91, Z = ‐5.5, p < 0.0001), whereas there was no significant difference with pretraining for MK‐6240 models (AUC = 0.84 vs. 0.83, Z = 0.47 ,p = 0.6). Visually, there appears to be greater separation in model learned features following pretraining in the AV‐1451 model. Conclusion: 3D pretraining for neural networks involving PET imaging could be accomplished with related but structurally different radioligands, though more work is needed into determining why certain pretraining experiments are successful. This procedure can enable better training of complex models with fewer constraints due to sample size. [ABSTRACT FROM AUTHOR]

Background: Glial activation may precede regional tau progression through Braak stages in Alzheimer's Disease (AD)[1]. However, the role of glial activity in the temporal progression of AD is not well characterized. Method: Nineteen controls and 22 patients with clinical diagnosis of MCI or AD underwent [18F]florbetaben, [18F]MK‐6240, and [11C]PBR28 PET to measure β‐amyloid, tau, and TSPO (a glial biomarker). Standard uptake value ratios and corrected gray matter volume (%GM) were calculated from prefrontal, temporal, and parietal regions to create composite AD‐centric measures for β‐amyloid, TSPO, and neurodegeneration. Braak‐specific gray matter volumes were used to measure tau and neurodegeneration in early (I/II), middle (III/IV) and late (V/VI) Braak regions. Tau and TSPO were measured with (‐PVC) and without partial volume correction. Correlation analyses were performed between TSPO and β‐amyloid, tau, and MMSE scores. Mediation analyses explored the effect of TSPO in the steps along a proposed model for AD progression (Fig 1). Result: Greater TSPO‐PVC was associated with greater β‐amyloid (std.β=0.38, p=0.015), and with greater tau‐PVC in middle (std.β=0.45, p=0.004) and late Braak regions (std.β=0.50, p=0.002, Fig 2), even after controlling for %GM. Greater TSPO‐PVC was associated with lower MMSE score (std.β=‐0.53, p=0.006). Similar results were observed for uncorrected PET data. TSPO‐PVC mediated the relationship between tau‐PVC in early Braak regions and tau‐PVC in middle Braak regions (indirect effect=0.118, p=0.017) and the relationship between tau‐PVC in middle Braak regions and neurodegeneration in middle Braak regions (indirect effect=‐0.255, p=0.027). TSPO‐PVC mediated the relationship between neurodegeneration and impaired MMSE performance for early (indirect effect=0.233, p=0.021) and middle Braak regions (indirect effect=0.204, p=0.029), and between neurodegeneration and impaired Trails B performance for early (indirect effect=0.283, p=0.009) and middle Braak regions (indirect effect=0.228, p=0.017). Conclusion: TSPO is related to β‐amyloid burden, tau pathology in middle and late Braak regions, and cognitive impairment in a manner independent of neurodegeneration. Our results suggest that glial activity impacts the progression of tau pathology from early to middle Braak regions and influences the relationship between neurodegeneration and cognitive impairment in these same regions. Reference: 1. Pascoal, T.A., et al. Microglial activation and tau propagate jointly across Braak stages. Nat Med27, 1592–1599(2021). [ABSTRACT FROM AUTHOR]

Background: 18F‐Florbetaben (FBB) is a PET radioligand approved for determining binary amyloid status. According to the product label, FBB images may be acquired anytime 45‐130 minutes post injection. Protocols vary in the acquisition of FBB with respect to use of Early (50‐70 minutes‐post‐injection) or Late (90‐110 minutes‐post‐injection) frames. Identifying differences between Early and Late acquisitions of FBB data could aid in image interpretation and inform efforts towards harmonization across cohorts. We evaluated inter‐rater agreement of visual reads (VR) and differences in SUVR thresholds to characterize Early vs. Late frames. Method: Fifty‐three subjects were studied including 23 patients meeting clinical criteria for amnestic mild cognitive impairment or mild Alzheimer's disease and 30 cognitively normal controls. FBB images were acquired 50‐110 min post‐injection in 5‐min frames. Images were corrected for subject motion and separated into 50‐70 and 90‐110 min time windows. Three certified readers blind to cognitive status and time window provided a binary VR for each of the 106 data sets. Global standardized uptake value ratios (SUVRs) were calculated using a cerebellar gray matter reference. An ROC‐derived SUVR cut‐off value was determined for each window using a preliminary cohort (n=23) and evaluated in a larger cohort (n = 53). Result: Inter‐reader agreement was similar in 50‐70 min and 90‐110 min scans, (Fleiss Kappa= 0.597 and 0.598). SUVRs from 50‐70 min and 90‐110 min scans were highly correlated (r = 0.99, p<0.0001). SUVRs were greater in visually positive than in visually negative scans for both time windows (Fig. 1; p<0.0001). SUVRs were still increasing at 50‐70 min but became but plateaued at 90 min. ROC‐derived SUVR cut‐off values (Fig. 1; SUVR50‐70=1.342, SUVR90‐110=1.524) largely agreed with the majority consensus VR (49/53; 92.5%) across both time frames. Conclusion: Early and Late frame data were highly correlated, and both provided robust quantitative differences between visually positive and negative Ab groups. However, imaging at 90‐110 min provides more stable SUVRs and therefore is preferred for measuring longitudinal change in amyloid burden. [ABSTRACT FROM AUTHOR]

Background: PET amyloid [11C]PiB imaging has revealed early detection of Ab in the striatum in individuals with Down Syndrome (DS), a group carrying genetic risk for AD. The goal of this investigation is to examine striatal binding of [11C]PiB and [18F]florbetapir (FBP) to determine if similar striatal uptake profiles are observed in individuals with DS. Methods: PET scans were acquired as part of the Alzheimer's Biomarker Consortium – Down Syndrome (ABC‐DS) study. DS participants were scanned with either PiB (n=204; ages 25‐56 yrs) or FBP (n=84; ages 40‐85 yrs) at seven academic sites. SUVR images were created with acquisition times of 50‐70min for both radiotracers, using a cerebellar reference region. ROIs were defined for the anterior ventral striatum (AVS) and the full cortex (CTX). SUVR was then converted to centiloids (CL) using published methods (Klunk 2015) with DS‐specific modifications, including AVS extraction and approximate CL_AVS. Aβ(+) was defined using a threshold of 18.1 CL in CTX ROI, categorizing Aβ(‐) and Aβ(+) groups. Comparisons across PiB and FBP examined 1) the proportion of CL_AVS values exceeding the threshold in the Aβ(‐) group and 2) the difference between SUVR_AVS and SUVR_CTX in the Aβ(+) group. Results: 58/204 subjects (PiB) and 72/84 subjects (FBP) were Aβ(+), with differences in Aβ(+) presumably due to greater age and advanced neurodegeneration in the FBP group. For the Aβ(‐) comparison, elevated CL_AVS was detected in 16/146 (PiB) and 0/12 (FBP). The average CL_AVS of those that exceeded the threshold was 37.0±12.7 CL (PiB) (mean±SD). For the Aβ(+) comparison, 52/58 (PiB) and 52/72 (FBP) had CL_AVS greater than their CL_CTX. The average difference between SUVR_AVS and SUVR_CTX for the Aβ(+) group was 0.50±0.39 SUVR (46.9±36.8 CL) (PiB) and 0.08±0.18 SUVR (14.2±31.0 CL) (FBP). Conclusions: PiB PET exhibited higher uptake in the striatum during early stages of amyloid deposition. PiB revealed a 6x greater difference than FBP between striatal to cortical SUVR in the Aβ(+) group. Neuropathological investigation is ongoing to characterize these differences in striatal binding in DS. Despite this difference in early‐stage binding, both PiB and FBP demonstrate sensitivity for determining the presence of Aβ in DS. [ABSTRACT FROM AUTHOR]

Background: Subjective Cognitive Decline (SCD), a potential marker of pre‐clinical AD, is frequently linked to mood disturbances, such as depression and apathy. These associations are thought to hinder the utility of SCD as a predictor of impending disease. However, recent evidence suggests that depression and apathy are separate constructs that may pose differential risk for cognitive impairment and neurodegeneration. Further, some studies have reported apathy to be a more sensitive predictor of cognitive decline than depression. Therefore, understanding the associations between SCD and mood will help refine SCD's utility as a marker of pre‐clinical AD. This study examines those associations with particular focus on whether apathy mediates the association between depressive symptoms and SCD. Method: 151 cognitively normal older adults (68% female, mean age 73±7) were assessed for level of SCD, apathy, and depressive symptoms using a 20‐item age‐anchored SCD questionnaire (i.e., "in comparison to others your age"), the Apathy Evaluation Scale and the Geriatric Depression Scale (GDS), respectively. Regression analyses examined the main effects of apathy and depressive symptoms on SCD, and apathy as a mediator between SCD and depressive symptoms. Regression analyses were also conducted after removing three GDS items previously shown to form an apathy factor. Result: Apathy (b=.84, p=.001) and depressive symptoms (b=4.57, p=.002) independently predicted SCD. In the mediation model, depressive symptoms predicted apathy (b=2.91, p<.001) but not SCD (b=2.14, p=.183). Apathy mediated the relationship between depressive symptoms and SCD (Indirect effect: b=2.44 SE=.80, CI(1.07, 4.30), accounting for 53% of the total effect. Apathy still remained a significant mediator even after eliminating the apathy‐related items from the GDS (Indirect effect: b=1.52 SE=.65, CI(.40, 2.92)), accounting for 40% of the total effect. Conclusion: Apathy mediated the relationship between depressive symptoms and memory complaints, even when the GDS only included the dysphoric and anxious elements of depression. These results underscore the relevance of apathy in preclinical stages of disease and is in line with previous studies indicating apathy's unique association with cognitive impairment and neurodegeneration. Longitudinal studies are required to further disentangle the temporality and potential additive risk for dementia of SCD and apathy. [ABSTRACT FROM AUTHOR]

Background: Individuals with Down syndrome (DS) have a higher likelihood of developing early‐onset Alzheimer’s disease which has been associated with abnormal tau proteins and atrophy in the brain [1]. Prior research has shown a spatial relationship between tau and atrophy in neurotypicals with dementia [2]. Although tau and atrophy certainly contribute independently to dementia, the synergistic relationship in a non‐demented population with DS is not well understood. This study aims to identify cortical regions with high tau and atrophy in aged, non‐demented participants with DS. Method: Analysis included 28 non‐demented participants (49.8 +/‐ 6.4 years; 17 males) with DS from the Alzheimer’s Disease in Down Syndrome (ADDS) study. Tau PET (18F‐AV1451) and MRI scans were acquired within the same timeframe (1.8 months +/‐1.5). Gray‐matter cortical ribbons were extracted from T1 MRI segmentations with Freesurfer (RRID: SCR_001847). The cortical ribbons were used to mask the coregistered PET scan data and converted to standard uptake value ratio (SUVR) units using the cerebellar cortex reference region. The tau and gray‐matter images were converted to z‐score images using the group mean and standard deviation. We defined high tau as voxels with z‐scores >=2.0 and high atrophy as voxels with gray matter z‐scores <= ‐2.0. The z‐score images were spatially normalized into MNI space with ANTs (RRID: SCR_004757) and a voxel‐based correspondence analysis was performed. Voxels surviving the high tau and high atrophy thresholds were evaluated to determine anatomical localization using the Desikan/Killiany atlas [3]. Result: We found high correspondence between tau and atrophy in the following regions (figure 1): entorhinal cortex, insula, fusiform, pars orbitalis, paracentral, precentral, superior temporal, medial orbitofrontal, lateral orbitofrontal, temporal pole, rostral middle frontal, superior frontal, pars triangularis, lingual, and the amygdala. Conclusion: Our study demonstrated a correspondence between atrophy and tau in deep cortical structures, as well as frontotemporal regions in aged, non‐demented adults with DS. Many of these regions are consistent with findings in neurotypical populations and shown in our prior work to be regions associated with increased amyloid (18F‐AV‐45) as a function of disease severity in DS [4]. [ABSTRACT FROM AUTHOR]