Standardized Uptake Value Ratio Research Articles

Examining the interplay between AD pathology and intracellular/extracellular properties of cortical tissue in preclinical AD

AbstractBackgroundAD pathology affects neuronal microstructural decades before clinical manifestation of the disease. Amyloid plaques and tau tangles are key contributors to alterations in brain microstructural properties such as neurite morphology and axonal transport and packing, among others. It takes many years for these microstructural changes to build up and be reliably detected using volumetric MRI measures of neurodegeneration. Here, we examined the utility of neurite imaging – a biologically inspired model for examining cortical microstructure – to examine the impact of amyloid and tau pathology on intra‐ and extra‐cellular properties of cortical tissue and to further improve our mechanistic understanding of AD development.MethodWe utilized multimodal imaging data collected as part of ADNI‐3 including structural, multi‐shell diffusion MRI, and amyloid (18F‐florbetapir) and tau (18F‐flortaucipir) PET. The sample included 90 cognitively normal older adults (CN:23/67 amyloid‐positive/negative, age = 72.0(8.8) years) and 65 older adults with mild cognitive impairment (MCI: 36/29 amyloid‐positive/negative, 73.3(7.2) years). Neurite orientation dispersion and density imaging (NODDI) measures including intracellular volume fraction (ICVF), neurite orientation and dispersion index (ODI) in gray matter, standardized uptake value ratio (SUVR) for amyloid/tau PET and cortical thickness were extracted for 360 regions defined by surface‐based HCP atlas. We performed mediation analyses to examine the interplay between AD pathology and intracellular/extracellular properties of brain tissue and also compared the clinical utility of cortical neurite measures against volumetric MRI in detecting brain changes associated with AD pathology (tau burden) in preclinical AD.ResultMediation analyses showed that tau burden mediates the impact of amyloid on intracellular properties of brain tissue (ICVF) in medial and lateral temporal structures (Fig.1A) whereas ODI is a significant mediator of the effect of extracellular amyloid accumulation on intra‐cellular tau burden in entorhinal cortex (Fig.1B) in amyloid‐positive older adults. Further, among the regions with highest tau burden in amyloid‐positive CN group, ICVF changes in lateral temporal cortex successfully distinguished preclinical AD from amyloid‐negative CN and was associated with tau pathology.ConclusionOverall, these preliminary results suggest that accumulation of amyloid plaques results in reduction of neurite complexity while build‐up of tau reduces the intracellular volume fraction in preclinical stage of AD.

Evaluation of the association between circulatory markers of neuroinflammation and Alzheimer’s disease pathology

AbstractBackgroundIncreasing evidence suggests that neuroinflammation and astrogliosis have a prominent role in the spread of pathology and the development of cognitive impairment in Alzheimer’s disease (AD). Circulating plasma markers of neuroinflammation, such as the glial fibrillary acid protein (GFAP), have recently become available, and early observations suggest a robust association with brain pathology. Positron emission tomography (PET) is a molecular imaging technique that allows visualisation and quantification of AD pathology in vivo. The aim of this study was to evaluate the association between GFAP and AD pathology, and its interaction with cognitive decline.Method121 subjects from the Geneva Memory Centre underwent amyloid and tau PET, blood collection, and mini‐mental state examination (MMSE). Amyloid uptake was calculated as centiloid whereas tau was quantified as standardised uptake value ratio. Association of centiloid and tau with GFAP was assessed using a multivariate linear regression model, corrected for age, gender, and MMSE. Mediation analysis described the mediating effect of GFAP on the association between centiloid and tau. Ninety‐four subjects had a follow‐up MMSE after at least 12 months. Linear regression was used to assess the effect of GFAP on the annual rate of change of MMSE (corrected for centiloid, global tau, age, and gender). Mediation analysis was used to test whether the relationship between rate of MMSE change and tau could be explained by a mediation of GFAP.ResultThe mean (SD) age of the participants was 72.6 (7.6) years, and 61 of 121 subjects were men. GFAP was significantly associated with increased tau, but not with centiloid. GFAP partially mediated the effect of centiloid on global tau (mediated proportion = 14%, p = 0.03). The rate of MMSE change was significantly associated with GFAP, and global tau, but not with centiloid. Moreover, GFAP partially mediated the effect between global tau SUVR and change of MMSE scores (52%, p<0.01).ConclusionElevated GFAP is associated with tau. GFAP also partially explains the effect of amyloid pathology on tau accumulation and of tau pathology on subsequent cognitive decline. These results support neuroinflammation and astrogliosis as a relevant contributor to AD pathology, which can be monitored in blood.

Association of Type‐2 diabetes mellitus with voxelwise tau uptake patterns

AbstractBackgroundDysfunctional insulin signaling and elevated hemoglobin‐A1c (HbA1c) ‐ both associated with type‐2 diabetes mellitus (T2DM) ‐ may result in increased tau hyperphosphorylation, thereby increasing the risk of Alzheimer’s Disease (AD)‐related neuropathology in T2DM patients. Individuals with both AD and T2DM show different profiles of tauopathy compared to individuals with just AD. Here, we evaluated voxelwise tau‐PET patterns with and without T2DM to clarify T2DM‐associated patterns of tau deposition in cognitively intact older adults.MethodWe examined 502 cognitively intact adults from the Health and Aging Brain Study: Health Disparities who had available 18F‐PI‐2620 tau‐PET, florbetaben‐PET (Aβ) (n = 462), MRI T1‐weighted scans (Siemens‐3T), and blood HbA1c levels (n = 492) (Table1). Voxelwise regression models were run in SPM12 using warped 18F‐PI‐2620 standardized uptake value ratio (SUVR) images as the outcome variable (gray matter of the posterior cerebellum reference region) and covarying for age, sex, and education. The first model assessed the effect of T2DM consensus diagnosis on tau uptake. The second model examined the relationship between HbA1c and tau uptake. Both voxelwise regression models were rerun adding global Aβ‐SUVR, obesity, and hypertension as covariates (Figure1).ResultVoxelwise regressions were corrected at voxel p<0.001, cluster FWE p<0.05. T2DM diagnosis was associated with higher 18F‐PI‐2620 uptake in the medial and inferior occipital regions, posterior cingulate, and parahippocampus. Higher HbA1c levels were associated with more 18F‐PI‐2620 uptake in parietal and frontal regions. When covarying for global Aβ‐SUVR, obesity, and hypertension, T2DM diagnosis was associated with 18F‐PI‐2620 uptake in the posterior cingulate. In the HbA1c model, no clusters survived FWE correction after covarying for global Aβ‐SUVR, obesity, and hypertension (Figure1). ConclusionIndependent of common T2DM comorbidities and brain amyloid, T2DM subjects demonstrated a 18F‐PI‐2620 tau uptake pattern that was different from the characteristic age‐associated medial temporal lobe pattern. Our voxelwise approach allowed us to identify regions that may distinguish T2DM‐related pathology from AD‐related changes. The difference in significant clusters between models could be due to HbA1c levels being controlled by medication in some T2DM patients – an area for future study.

The aftermath of boxing: Longitudinal changes of cerebral retention patterns of 18F‐florzolotau (18F‐APN‐1607) in the former professional boxers

AbstractBackgroundChronic traumatic encephalopathy (CTE), formerly known as “boxer’s encephalopathy” is a neurodegenerative disease associated with mild‐repetitive TBI. CTE is characterized by the deposition of hyperphosphorylated tau as neurofibrillary tangles around small blood vessels of the cortex, typically at the sulcal depths. Although tau PET studies on American football players reported findings consistent with neuropathological changes identified by postmortem brains, there are no reports of tau PET studies in boxers, and longitudinal changes of tau depositions following boxing remains unknown. The present study was aimed to characterize cerebral retention patterns of 18F‐florzolotau (18F‐APN‐1607) in the former professional boxers.MethodTwenty‐nine former boxers (43.4 ± 10.3 years) and 27 age‐matched healthy control (HC) subjects underwent PET scans with 18F‐florzolotau and 11C‐PiB. Eleven former boxers underwent a second tau PET scan (average interval: 874days). Standard uptake value ratio (SUVR) with the optimized reference tissue was calculated. Regions of interest were defined on brain regions of the gray matter (GM), white matter (WM) and the gray‐white matter boundary (GWB). Association of regional SUVR values with neurocognitive symptoms and various indexes related boxing was investigated.ResultOne boxer was [11C]PiB positive. Former boxers showed higher 18F‐florzolotau retentions than HC subjects in the bilateral frontal GM, right parietal GM, bilateral frontal WM, right parietal WM (P < 0.001). There was a significant correlation between SUVR values in the whole GM and the total number of bouts (p = 0.07,r = 0.40). Subtyping using z‐score heatmap with predetermined criteria revealed heterogeneity in 18F‐florzolotau retention patterns in boxers. Of the 29 boxers, (1) 10 showed increased retention of 18F‐florzolotau in GM (GM dominant), (2) 4 showed increased accumulation mainly in WM (WM dominant), (3) 5 showed increased accumulation in GWB (GWB dominant), and (4) 10 showed no significant increased accumulation in the brain (No tau). These types vary in longitudinal changes of 18F‐florzolotau retention patterns, as well as in clinical features.ConclusionThe present study not only supports pathological heterogeneity of late‐sequalae of professional boxing, but also suggests utility of 18F‐florzolotau PET in detection of late‐life neurodegenerative diseases following mild‐repetitive TBI.

The association between microstructural MRI biomarkers and sequential amyloid and tau‐related cortical changes in asymptomatic elderly individuals

AbstractBackgroundIn Alzheimer’s disease, early amyloid‐β and tau deposition may have differential downstream effects on synaptic function and neuronal loss in a stage‐dependent manner. Amyloid‐related synaptic changes are thought to precede tau accumulation, which starts in the medial temporal lobe before propagating and leading to neurodegeneration. We explored whether microstructural MRI measures can detect these different sequential cortical changes.MethodSeventy‐nine healthy, asymptomatic individuals, recruited from the Insight 46 study of the British 1946 birth cohort underwent combined PET/MR with [18F]florbetapir Aβ‐PET at ∼73yrs, and [18F]MK‐6240 tau‐PET at ∼76yrs. Standard uptake value ratios were calculated using a whole cerebellar reference for florbetapir and an inferior cerebellar reference for MK‐6240. Multi‐shell diffusion MRI was acquired; neurite orientation dispersion and density imaging was used to quantify neurite density (NDI) and orientation dispersion (ODI, a proxy measure of dendritic morphology/complexity), with DTI measuring mean diffusivity (MD), a less specific measure of degenerative change. PET and microstructural MRI biomarkers were assessed in a neocortical composite ROI and across Braak stages.ResultThirty‐six participants were amyloid positive, 18 of whom were tau positive (eight Braak stage 1‐2, four Braak 3‐4, six Braak 5‐6). Across all participants, in the neocortical composite, significant differences between amyloid positive and amyloid negative individuals were found in both tau PET (p = 0.0092) and ODI (p = 0.030) but not for other measures. Within the amyloid positive group, in the cortical composite, significant differences in MD (p = 0.0093) but not ODI were seen when comparing those who were tau positive and negative. In the amyloid positive group, there was an association between tau PET and MD in the MTL (Braak 1‐2) (r = 0.34, p = 0.040) and Braak 3‐4 (r = 0.37, p = 0.025), with an association between tau and ODI (r = 0.48, p = 0.0031) in Braak 3‐4 only. After adjusting for volume, only the association between tau PET and ODI in Braak 3‐4 remained (p = 0.038).ConclusionOur results are consistent with amyloid deposition causing dendritic/changes (as assessed by ODI) with consequent synaptic dysfunction. Later tau propogation is then associated with frank neuronal breakdown (as measured by MD). dMRI cortical imaging biomarkers have the potential to provide proxy measures of underlying AD stage/pathology prior to symptom onset.

Inflammatory and metabolic responses in the aging rat brain

AbstractBackgroundThe brain is an energetically demanding organ that mostly relies on glucose oxidation to sustain its sophisticated networks. Transcriptomic studies suggest metabolic‐related genes are downregulated in the aging brain but inflammation‐related genes are highly upregulated. Astrocytes are key mediators of brain inflammation and metabolism. Yet, the relationship between inflammatory signals and energy substrates consumption in the aging brain remains unclear. To address this question, we investigated the response of the young and aged rat brain metabolism to a nonsteroidal anti‐inflammatory (NSAID) drug.MethodFive young adults (4 mo), and five aged (24 mo) male Wistar rats were scanned using positron emission microtomography with 18F‐fluorodeoxyglucose ([18F]FDG‐PET). Scans were done under basal conditions or after three‐day treatment with 5mg/kg ketoprofen (i.p.), an NSAID known to cross the blood‐brain barrier. Standard uptake values ratios (SUVr, pons as reference) were calculated for the main brain regions. Synaptosome and total mitochondria preparations from the cortex had their oxygen consumption rates measured in an Oroboros Instrument, using a SUIT protocol. Results were analyzed by t‐test, with statistical significance P < 0.05.ResultUnder basal conditions, glucose uptake showed no differences between young and aged rats. After ketoprofen treatment, clusters of glucose hypermetabolism appeared in the cortex and hippocampus of the adult but not the aged brain. Synaptosomal and mitochondrial preparations did not present differences in ATP‐linked, maximal, or leak respiration between groups under basal conditions. However, aged synaptosomes (t = 3.417, p = 0.0112) and total mitochondria (t = 3.131, p = 0.0166) presented higher respiratory coupling ratios.ConclusionWe previously showed that [18F]FDG‐PET signal highly depends on astrocyte function. The lack of response to ketoprofen in aged rats may be related to astrocyte dysfunction in aging. As a counter‐response to the aging process, the brain mitochondria are highly coupled, keeping the system as efficient as necessary. Our findings indicate that brain metabolism might not be coupled to inflammation in the aged rat brain. Still, we identified a remarkable plasticity of the mitochondria, which can sustain physiological responses despite the aging process.

Association between brain amyloid deposition and longitudinal changes of white matter hyperintensities

AbstractBackgroundWhite matter injury had been mostly considered a result of chronic ischemia caused by cerebral small vessel disease. However, recent studies reported that white matter degeneration could also be an important pathophysiological feature in Alzheimer’s disease (AD). This study aimed to identify the relationship between AD biomarkers, particularly amyloid‐beta (Aß) and tau deposition, with white matter hyperintensities (WMHs) representing cerebral white matter injury.MethodA total 456 older adults aging from 55 to 90 years including cognitively normal and cognitively impaired individuals were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease (KBASE) cohort. The participants underwent comprehensive clinical and neuropsychological assessment, [11C] Pittsburgh Compound B PET for measuring Aß deposition, [18F] AV‐1451 PET for measuring tau deposition, and MRI scans with fluid‐attenuated inversion recovery image for measuring WMH volume. For PET scans, standard uptake value ratio (SUVR) was used for the analyses; combined regions of hemispheric white matter, inferior cerebellum and pons were used as the reference region when obtaining SUVRs. The relationships between Aß or tau deposition and WMH volume were examined using multiple regression analysis. In this analysis, baseline Aß or tau were used as independent variables, and baseline as well as change of WMH volume over 2 years were used as dependent variables.ResultBaseline Aß deposition levels had significant positive association with longitudinal change of WMH volume over 2 years (ß = 0.238, p = 0.009), but not with WMH volume at baseline. Baseline tau was not related with any of WMH volume at baseline and longitudinal change of WMH volume over 2 years. We also found significant interaction effect between baseline Aß deposition level and sex on longitudinal change of WMH volume. Subsequent subgroup analyses showed that high baseline Aß deposition was associated with increase of WMH volume over 2 years in female (ß = 0.427, p = 0.001), but not in male.ConclusionOur findings suggest that Aß deposition, but not tau, accelerates cerebral white matter injury, particularly in female.

White matter hyperintensity volume changes are associated with progressive hippocampal atrophy in Insight 46: the neuroscience sub‐study of the MRC National Survey of Health and Development, the British 1946 birth cohort

AbstractBackgroundWhite matter hyperintensity volume (WMHV) is a presumed marker of cerebrovascular disease associated with ageing, Alzheimer’s disease (AD) and vascular dementia. Higher baseline WMHV is associated with increased brain atrophy rate, with disproportionate effects in the hippocampus – a region affected early in AD. Understanding the relationships between longitudinal change in WMHV and hippocampal atrophy, while accounting for AD and cardiovascular risk factors, will increase our understanding of the multifactorial disease processes occurring during the early stages of AD.MethodRate of change in WMHV was calculated using Bayesian Model Selection (BaMoS) and total hippocampal atrophy rate was calculated using the Boundary Shift Integral (BSI), measured concurrently between two time‐points. Participants with major neurological disorders were excluded. Linear regression was used to test whether rates of WMHV change predicted rates of hippocampal atrophy while adjusting for total intracranial volume (TIV), sex and age. Further models adjusted for baseline PET amyloid‐β (Aβ) standard uptake value ratio (SUVR), ApoE‐e4 carrier status, and Framingham heart study cardiovascular risk (FHS‐CVS) aged 69, and tested for interactions with rate of WMHV change.Result308 individuals with high‐resolution isotropic MRI data at two time‐points were analysed in this study (Table 1). For every 1ml/year increase in WMHV there was an associated 0.013ml/year decrease in total hippocampal volume (Table 2, Model 1). This association remained when adjusting for Aβ SUVR (Table 2, Model 2), ApoE‐e4 status (Table 2, Model 3), and FHS‐CVS (Table 2, Model 4). A separate semi‐partial correlation analysis showed 4.6% of variance in rate of hippocampal atrophy was uniquely explained by rate of WMHV change when accounting for TIV, sex and age. There were no significant interactions between rate of WMHV change and Aβ, ApoE‐e4 or FHS‐CVS in relation to hippocampal atrophy rate.ConclusionIncreases in WMHV were significantly associated with progressive hippocampal atrophy accounting for head size, age and sex. These associations were not materially changed when adjusting for amyloid deposition, ApoE‐e4 or cardiovascular risk factors. These results suggest systemic processes that contribute to WMH development also contribute to hippocampal atrophy. Further work is required to determine the processes that drive this relationship.

Effects of β‐amyloid deposition, dopaminergic depletion, and perfusion on clinical features in cognitively impaired patients

AbstractBackgroundAlthough mixed pathologies are common in Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB), effects of β‐amyloid and dopaminergic depletion on brain perfusion and clinical features have not been elucidated.MethodIn 99 patients with cognitive impairment due to AD and/or DLB and 32 control participants, 18F‐Florbetaben (FBB) PET, dual‐phase dopamine transporter (DAT) PET, and brain MRI were performed to measure β‐amyloid standardized uptake value ratio (SUVR), dopaminergic depletion in the ventral striatum, caudate, and putamen, and regional brain perfusion. Cognitive score, visual hallucination (VH), cognitive fluctuation (CF), and rapid eye movement sleep behavior disorder (RBD) were evaluated. Relationship among global FBB‐SUVR, striatal DAT uptakes, regional brain perfusion, clinical symptoms, and cognitive scores were evaluated using general linear or logistic regression models as appropriate.ResultGlobal and mean lobar FBB SUVRs negatively correlated with DAT uptakes in the ventral striatum and caudate but not with putamen DAT uptake. Lower ventral striatal DAT uptake was associated with right temporo‐parietal hypoperfusion and hyper‐perfusion in the anterior cingulate cortex, vermis, motor cortex, and hippocampus, while higher FBB SUVR was associated with hypoperfusion in the left entorhinal cortex and temporo‐parietal cortex. Lower ventral striatal DAT uptake increased the risks of VH and RBD directly and indirectly through the mediation of hippocampal hyper‐perfusion, while hippocampal hyper‐perfusion fully mediated the effect of lower ventral striatal DAT uptake on the risk of CF. Higher FBB SUVR and lower DAT uptake were associated with language, memory, and executive dysfunction directly and indirectly through the mediation of regional perfusion, while regional perfusion fully mediated their effects on visuospatial dysfunction.Conclusionβ‐amyloid deposition and striatal dopaminergic depletion reflecting AD‐ and DLB‐related neurodegenerations are inter‐correlated but differently contribute to regional perfusion changes and cognitive dysfunction.

Feasibility Study for Longitudinal Retinal Imaging in the Anti‐Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) Trial

AbstractBackgroundAlterations to the retina manifest in patients diagnosed with neurodegenerative diseases such as Alzheimer’s disease (AD). Retinal imaging techniques open the possibility for non‐invasive evaluation of AD pathology. Clinically AD diagnosed patients exhibit retinal spots that may be amyloid deposits. Few studies monitoring preclinical individuals exist, limiting the assessment of the feasibility of retinal imaging as a biomarker for early‐stage AD risk detection.MethodWe compared extent and distribution of retinal fluorescent spots and cerebral amyloid in clinically normal individuals who screened positive for high amyloid levels through positron emission tomography (PET) from the Anti‐Amyloid Treatment in Asymptomatic Alzheimer Disease (A4) as well as a companion cohort of individuals who exhibited low levels of amyloid PET in the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) study. We quantified the number of curcumin‐positive fluorescent retinal spots from participants from both studies to determine retinal amyloid deposition at baseline.ResultParticipants from the A4 trial exhibited a greater number of retinal spots compared to those from the LEARN study. We report a positive correlation between retinal spots and brain amyloid, as measured by the standardized uptake value ratio (SUVr).ConclusionThe results of this study support the use of retinal fundus imaging for and assessing relationships with brain amyloid PET SUVr. Further work is needed to determine the nature of retinal spots and their relationship with actual amyloid plaques cross‐sectionally and longitudinally.

Tangle and neuritic burden from neurofibrillary tangle maturity markers in the hippocampus are differentially correlated to tau‐PET, MRI, and cognitive measures

AbstractBackgroundThere is a gap in knowledge concerning which tangle maturity levels (Moloney, Alzheimers Dement, 2021) are associated with antemortem measures (tau‐PET, MRI, cognitive testing). We hypothesized antemortem measures would be more strongly correlated with markers recognizing middling to advanced tangle maturity. We additionally sought to assess the strength of the relationship with tangle and neuritic pathology recognized by tangle maturity markers.MethodPosterior hippocampi from n = 30 cases with antemortem measures ≤3 years prior to death were cut (5µm) and immunostained with tau antibodies to evaluate early (AT8), middling (PHF‐1), or advanced (2E9) tangle maturity. GENIE (pattern recognition software) separated tangle from neuritic burden (Figure). Hippocampus, parahippocampus, and fusiform were analyzed to correlate digital pathology with flortaucipir tau‐PET standard uptake value ratio (SUVr, cerebellar crus normalization), MRI volume/thickness, and cognitive measures which included Auditory Verbal Learning Test (AVLT) delayed recall, Mini‐Mental State Exam (MMSE), and Clinical Dementia Rating (CDR).ResultThe strongest relationships observed with flortaucipir were with PHF‐1 and 2E9 burden (Table). AT8 tangle and neuritic burden correlations with flortaucipir were relatively similar in the hippocampus but were stronger with neuritic pathology in parahippocampus and fusiform. PHF‐1 tangle and neuritic burden correlations were relatively similar with flortaucipir. 2E9 correlations were stronger with neuritic pathology in hippocampus. MRI measures were more strongly correlated with PHF‐1 and 2E9 burden. AT8 tangle and neuritic burden were correlated with fusiform thickness. The correlations between MRI measures and 2E9 tangle and neuritic burden were similar. Correlations between cognitive measures were similar between tangle maturity markers. AVLT delayed recall correlated with all tangle maturity markers, but neuritic burden was stronger for AT8 and 2E9 in hippocampus compared to tangle burden. MMSE correlated with neuritic burden for all tangle maturity markers. CDR remained statistically non‐significant.ConclusionInterpretation of the strength of the relationship between tau burden and antemortem measures differed across tangle maturity markers. Neuroimaging measures may be more sensitive to middle/advanced maturity markers with limited differences between tangle and neuritic pathology. These findings may be biologically relevant and imply either a limited recognition of tangles depending on antibody epitope, and/or may represent the clinicoradiologic impact of increasing neuritic burden.

Defining tau pathology and neurodegeneration by dual‐phase 18F‐Flortaucipir PET as diagnostic and prognostic biomarkers in a memory clinic setting

AbstractBackground 18F‐Flortaucipir‐PET allows visualization of tau deposits (T) representing a powerful diagnostic and prognostic tool for Alzheimer’s disease (AD). Dual‐phase 18F‐Flortaucipir‐PET can also evaluate neurodegeneration (N) through the early‐phase images. We aim to assess the diagnostic and prognostic power of visual and semi‐quantitative T assessment and the potential added value of early‐phase images.MethodWe included 245 subjects from the Geneva Memory Clinic who underwent 18F‐Flortaucipir‐PET (157 with a dual‐phase protocol). Amyloid (Aβ) status was available for 207 subjects and clinical follow‐up for 135. T positivity and a Braak staging estimate were evaluated visually and semi‐quantitatively by standardized uptake value ratio (SUVr) in AD‐related regions. N status was semi‐quantitatively defined using early‐phase images as a summary metric in the AD meta‐ROI region. Receiver Operating Characteristic (ROC) analyses were applied to compare T and N discriminative power. Linear mixed‐effects models tested the prognostic values of T alone and T combined with N (T/N profiles).ResultThe mean age of the participants was 70.49±9.13 years, 52.2% were females and 55.7% were Aβ+. 53.4% of subjects had Mild Cognitive Impairment (MCI), 19.8% had dementia and 26.7% were cognitively unimpaired. All T‐assessment modalities significantly discriminated Aβ+ MCI and demented subjects from others (AUC>0.80), Aβ+ from Aβ‐ subjects (AUC>0.85), and cognitive decliners from stable individuals at follow‐up (AUC>0.79). The discriminative power of N, assessed by early‐phase images, was lower compared to T, with AUC ranging from 0.63 in discriminating Aβ+ MCI and demented subjects from others to 0.71 for discriminating Aβ+ T+ from Aβ‐ T‐ subjects. Linear mixed‐effect models showed that T+ individuals presented a significantly faster cognitive decline than the T‐ group (p<0.001) as well as T+/N+ and T+/N‐ compared to T‐/N‐ group (p<0.001), independently from T classification methods.ConclusionOur results show a similar diagnostic and prognostic power of visual and semi‐quantitative T evaluation of 18F‐Flortaucipir‐PET, proving its high potential for clinical applicability. The N status assessment with early‐phase images did not improve the prediction of cognitive decline. Its usefulness for differential diagnosis by the topographical information provided should be investigated.

Association of FDG‐PET with co‐pathologies in autopsy‐proven AD

AbstractBackgroundThe presence of multiple pathologies is the rule and not the exception in Alzheimer’s disease (AD). The goal of this study was to assess whether 18F fluorodeoxyglucose (FDG) PET can provide information about the presence of non‐AD pathologies in autopsy‐proven AD patients.MethodOur cohort included 55 patients with antemortem FDG‐PET and autopsy‐proven intermediate/high ADNC from the UCSF Neurodegenerative Diseases Brain Bank. All patients underwent FDG‐PET. Standardized uptake value ratio (SUVR) images were computed using the pons as reference region. W‐maps (adjusted Z‐score maps) were created, comparing each patient to a group of 187 controls (age range: 20‐96, 40% males). Voxel‐wise correlations were computed to verify the unique contribution of each neuropathology to cerebral hypometabolism, while controlling for age, sex, time between FDG‐PET scan and death, scanner type, Thal and Braak staging, and the presence of all other co‐pathologies. Associations between SUVR values in specific region of interests (ROIs) were also assessed. The ROIs included were the following: Landau AD ROI for AD pathology, Cingulate Island Sign (CIS) for Lewy body disease, Inferior/Medial Temporal Lobe (IMT) and IMT/Frontal Supraorbital (IMT/FSO) ratios for limbic‐predominant age‐related TDP43 encephalopathy neuropathologic change (LATE‐NC).ResultMean age was 63.8 ± 10.5, mean time between FDG and death was 6.2 ± 2.8, and mean MMSE was 21.1 ± 7.1 at the time of PET (Table 1). Group averaged W‐map showed the typical temporo‐parietal AD pattern; hypometabolism in these regions was associated with higher Thal phase and Braak stage (Figure 1). Lewy body disease (LBD), argyrophilic grain disease (AGD), aging‐related tau astrogliopathy (ARTAG), cerebral amyloid angiopathy (CAA) and vascular brain injury (VBI) were associated with hypometabolism in small discrete regional clusters (Figure 2). The Landau AD ROI was associated with both Thal (rs = ‐0.325, p = .015) and Braak stages (rs = ‐0.447, p<.001). The CIS sign was not associated with the presence of LBD (t(53) = 1.01, p = 0.319). Both the IMT (t(53) = ‐2.25, p = .028) and IMT/FSO (t(53) = ‐3.40, p = .001) ratios were associated with the presence of LATE‐NC.ConclusionCo‐pathologies make a relatively small contribution to hypometabolism in patients with intermediate‐high ADNC. Novel biomarkers are needed for precise detection of co‐pathologies.

Harmonization of tau‐PET in Alzheimer’s disease: comparison of methods to derive CenTauR units for [18F]RO948, [18F]Flortaucipir, and [18F]MK‐6240

AbstractBackgroundTau positron emission tomography (PET) is increasingly used in the clinical evaluation of patients and as an outcome measure in Alzheimer’s disease (AD) clinical trials. Due to differences in tracer properties, instrumentation, and methods of analysis, however, tau‐PET outcome data cannot currently be meaningfully compared or combined. Here, we tested i) the feasibility of adapting the Centiloid method—an approach originally developed to standardize amyloid PET—to harmonize tau‐PET quantification (CenTauRs); ii) the performance of a non‐linear mixed model‐based approach (Joint Propagation Model) that does not require the use of a reference tracer.MethodHead‐to‐head tau‐PET data (Table 1) was included from two cohorts ([18F]RO948 vs [18F]flortaucipir, n = 37 [BioFINDER‐2]; [18F]flortaucipir [Avid A05] vs [18F]MK‐6240, n = 15, University of Pittsburgh) in which each participant was scanned with two tau‐PET tracers. Standardized uptake value ratio (SUVR) values were calculated using the inferior cerebellar cortex as the reference region. Anchor point data (Table 1) was derived for each tracer using the following criteria: CenTauR‐0, cognitively unimpaired (CU), amyloid PET negative (<10 Centiloids); CenTauR‐100, amyloid PET positive (>50 Centiloids), typical (temporoparietal) AD pattern on tau‐PET visual read, age<65 andMMSE>20. Regions‐of‐interest (ROIs) included a universal tau‐PET ROI—based on the intersection of tracer specific ([18F]flortaucipir, [18F]MK‐6240, [18F]PI‐2620, [18F]PM‐PBB3, [18F]GTP1 and [18F]RO948) masks that had been derived by subtracting average of amyloid‐negative CU images from the average AD image—as well as four subregions delineated within this ROI (medial temporal, meta‐temporal, temporoparietal and frontal) (Figure 1A). An overview of the adapted Centiloid‐like approach and the joint propagation model is shown in Figure 1B.ResultHigh R2 values were observed between tracers across all ROIs: [18F]RO948 vs [18F]flortaucipir, average 0.965 [range 0.923 (frontal) to 0.986 (universal)]; [18F]MK‐6240 vs [18F]flortaucipir, average 0.985 [range, 0.923 (medial temporal) to 0.991 (frontal)]. The Centiloid‐like and joint propagation model approaches provided near identical CenTauR values (Figure 2).ConclusionPreliminary findings support the development of standardized scale for tau‐PET using both a Centiloid‐like or joint propagation model approach. Additional data and consideration of the advantages and disadvantages of each will be needed to recommend one approach over the other.

In vivo data‐driven patterns of Tau accumulation associated with AD progression using 18F‐MK‐6240 PET

AbstractBackgroundAlzheimer’s disease is a neurodegenerative disease associated with accumulation of amyloid beta and tau neurofibrillary tangles following a pattern known as Braak stages (Braak 1995,2011). Recent papers (Pascoal 2020) indicate possibility to recapitulate Braak histo‐pathological stages in vivo using tau tangles PET tracer 18F‐MK‐6240, using manually defined regions of interest. This study analyzes patterns of Tau accumulation associated with AD in a completely data‐driven fashion.MethodWe used T1w MRI and 18F‐MK‐6240 Pet scans from healthy controls in the community or outpatients at the McGill University Research Centre for Studies in Aging. The following number of datasets from each diagnostic group [n scans total (m unique participants)]: cognitively normal: 347(194), mild cognitive impairment: 163(99), Alzheimer’s disease dementia: 114(77).T1w MRIs were pre‐processed (non‐uniformity correction, intensity normalization, stereotaxic registration, brain masking, tissue classification); FALCON (Fonov 2020) was used to extract the mid‐cortical surface.PET scans were linearly registered to the T1w MRI and the standardized uptake value ratio (SUVR) was calculated using whole cerebellum grey matter as the reference. SUVR values were sampled along cortical mid‐surfaces and geodesically smoothed with a 10mm gaussian and resampled to the cortical surface of MNI2009c template (Fonov 2011).ResultFigure 1 shows Mean SUVR values on top of Figure 1, and latent factors at the bottom. Overall low values of tau in the temporal, partial and occipital region are associated with normal cognition (F1). Tau changes in the temporal lobe (F2, similar to stage 1&2) are associated with a slight cognitive change. Change in parietal and posterior temporal areas (F3, similar to stage 3&4) are associated with even more cognitive loss. Finally changes in the motor strip and ventral portion of the occipital lobe and remaining cortex (F4 and F5) are associated with the very advanced stages (5&6) of cognitive decline.ConclusionWe discovered patterns of tau accumulation associated with progression of cognitive decline in a completely data‐driven fashion .

Tracing the spatial extent of cortical tau over time in Alzheimer’s disease

AbstractBackgroundNeuropathological studies defined the paradigm that tau—the pathological hallmark of Alzheimer’s disease (AD)—spreads homogenously across individuals from the medial temporal lobe to the neocortex. This staging system is referred to as Braak stages. However, neuroimaging studies have since highlighted significant inter‐regional and inter‐individual differences in tau spreading patterns. We investigated the spatial extent of cortical tau over time in the amnestic AD spectrum.MethodWe included 195 amyloid‐positive ADNI participants that had at least two tau‐PET scans (90 cognitively unimpaired [CU], 66 with mild cognitive impairment [MCI], 39 with AD dementia; average follow‐up 2.6 years). We derived regional thresholds of tau positivity for 64 cortical regions and the amygdalae using Gaussian mixture modeling and applied them to all tau scans. The same approach was used to obtain thresholds for composite regions corresponding to the Braak stages. For each region, we calculated the rate of change in the amount (i.e., standardized uptake value ratio [SUVR] change) of tau pathology and the annual change in the positivity status (i.e., spatial extent change). These rates were compared between diagnostic groups using linear mixed models.ResultAcross diagnostic groups, change in tau positivity followed Braak staging; over 90% of participants positive at follow‐up either progressed or were already positive on a previous Braak stage at baseline (Fig‐1AI) and greater SUVR change was observed in temporal regions (Fig‐1B). However, across the brain, there was heterogeneity between individual patterns of tau positivity progression (Fig‐1AII). This heterogeneity was most striking in MCI patients, where many regions progressed from low to high levels of tau (Fig‐1C). Regional SUVR change did not differ between participants with AD or with MCI (Fig‐2A), but participants with MCI showed the fastest change in regional positivity compared to participants with AD and CU participants. (Fig‐2B)ConclusionWhile tau propagation follows Braak staging, inter‐individual differences can be found by considering a fine‐grained approach of regional progression to positivity. Furthermore, participants with MCI have the highest number of regions becoming abnormal annually. Our research pushes the importance of considering the entire brain when studying change in tau pathology, particularly before AD diagnosis.

Employing transfer learning to optimize deep learning models for Alzheimer’s disease classification using two tau PET tracers

AbstractBackgroundDeep learning models, particularly convolutional neural networks (CNNs), have shown promise in Alzheimer’s disease (AD) classification using tau PET data. However, limited sample sizes and unharmonized tau tracers present challenges to developing an agnostic tau tracer tool to predict AD using machine learning. Transfer learning, which leverages pre‐trained models for related tasks, may address these issues. Here we evaluate the effectiveness of transfer learning in optimizing 3D CNNs for AD classification with distinct cohorts and tau tracers.MethodWe used tau PET images from ADNI ([18F]Flortaucipir, n = 437) and TRIAD ([18F]MK‐6240, n = 423) cohorts, categorizing patients into CU (cognitively unimpaired) and CI (cognitively impaired). Standardized uptake value ratios (SUVR) were used for tau PET data. Separate 3D CNNs were trained for each tracer, with SUVR volumes as input and diagnosis as output. For transfer learning, we trained a model on [18F]Flortaucipir data with a reduced learning rate, using a pre‐trained model from [18F]MK‐6240. Models underwent 5‐fold cross‐validation, and metrics were computed as the average of validation metrics across folds. To avoid data leakage, images from the same subject were assigned to the same fold.ResultThe model trained on [18F]MK‐6240 tracer demonstrated higher classification performance than [18F]Flortaucipir (AUC = 0.84 vs 0.67; Figure 1. F1‐score = 79.77% vs 64.66%; Table 1). To enhance the classification performance of [18F]Flortaucipir model, we employed a transfer learning approach by leveraging the model pre‐trained with [18F]MK‐6240. With this approach, we observed a slight improvement in all classification metrics compared to the model trained solely on [18F]Flortaucipir data (AUC = 0.71 vs 0.67; Figure 1. Accuracy = 71.39% vs 67.72, F1‐score = 67.97% vs 64.66%; Table 1).ConclusionThis finding highlights the value of transfer learning in optimizing deep learning models for Alzheimer’s disease classification, particularly when handling tau tracers with varying performance levels. Our results are consistent with previous on transfer learning’s effectiveness in this context. These preliminary findings indicate that applying this technique to larger datasets of tau tracers may further enhance model performance, potentially leading to the development of a tau tracer‐agnostic tool that overcomes the need of tracer harmonization for predicting dementia.

Association of β‐amyloid, microglial activation, hypermetabolism and hypertrophy in Alzheimer’s disease

AbstractBackgroundPlasma β‐amyloid (Aβ) Aβ42/Aβ40 measured by immunoprecipitation and liquid chromatography‐mass spectrometry assay has been demonstrated useful to identify Aβ PET positive individuals, but the concordance and discordance about the positivity of plasma Aβ42/Aβ40 (Plasma+/‐) and Aβ PET (PET+/‐) have not been fully characterized. Moreover, the association of Aβ deposition, cortical thickness (CTh), glucose metabolism, and microglial activation among different Plasma/PET profiles is poorly understood.MethodIn this study, we classified 173 ADNI participants into Plasma‐/PET‐ (N = 48), Plasma+/PET‐ (N = 43), and Plasma+/PET+ (N = 73) group based on thresholds of composite Aβ PET (18F‐florbetapir, FBP) standardized uptake value ratio (SUVR) (≥1.11) and plasma Aβ42/Aβ40 (≤0.1218). Part of them had longitudinal 18F‐fluorodeoxyglucose (FDG) PET, structural MRI, and CSF soluble Triggering Receptor Expressed on Myeloid Cell 2 (sTREM2) data. Baseline and slopes of FBP SUVRs of 68 Freesurfer‐defined ROIs were compared between different Plasma/PET groups. The associations of regional FBP SUVRs, CSF sTREM2, regional glucose metabolism (reflected in FDG‐SUVR), and CTh (measured using structural MRI) were investigated in different Plasma/PET groups.ResultPlasma Aβ42/Aβ40 might become positive earlier than Aβ PET (Fig.1A, McNemar’s test, p<0.001). Longitudinally, Plasma+/PET‐ participants showed faster rates of Aβ accumulation in bilateral BANKSSTS, supramarginal, and superior parietal cortices whereas demonstrated no difference in baseline FBP SUVRs than Plasma‐/PET‐ participants (Fig.1D‐E). Higher CSF sTREM2 level and faster rate of CSF sTREM2 increase were related to slower rates of Aβ accumulation in BANKSSTS (standardized β (βstd) = ‐0.418, p = 0.002) in Plasma+/PET+ but not in Plasma+/PET‐ individuals (Fig.2A and D). In contrast, thicker CTh (Fig. 4B‐C) and higher glucose metabolism (Fig. 4H‐I) in supramarginal and superior parietal cortices were coupled to faster rates of CSF sTREM2 increase in Plasma+/PET‐ rather than in Plasma+/PET+ individuals.ConclusionThese findings suggest that plasma Aβ42/Aβ40 might become abnormal earlier than Aβ PET, and plasma Aβ42/Aβ40 positive Aβ PET negative (Plasma+/PET‐) individuals accumulate Aβ plaques faster than Plasma‐/PET‐ individuals. Activated microglia may play a role in reducing fibrillar‐Aβ increasing rates in late amyloidosis stage, whereas it may be related to hypertrophy and hypermetabolism in early amyloidosis stages.

Quantifying tau deposition with the Parametric Estimation of Reference Signal Intensity: a 18F‐PI2620 tau‐PET and 4R‐tauopathy study

AbstractBackgroundTau positron emission tomography (PET) can detect abnormal tau in neurodegenerative diseases. Conventional reference regions show high PET signal variability and are susceptible to contamination from partial‐volume effects of tracer uptake. The parametric estimation of reference signal intensity (PERSI) has been introduced as a new method to overcome those issues in patients with Alzheimer’s disease. The current study investigates its applicability on patients with underlying 4R‐tauopathy with the radiotracer 18F‐PI‐2620.MethodIn this preliminary, cross‐sectional analysis, 9 patients with clinically suspected 4R‐tauopathy underwent T1‐weighted MRI and 0‐60 min dynamic 18F‐PI‐2620 PET scanning. PERSI identified voxels within the white matter without specific binding for use as a reference region, which was compared with conventional anatomically defined reference regions. Time‐activity curves (TAC) and standardized uptake value ratios in the time window of 20‐40 min (SUVr20‐40) were generated for regions of interest (frontal lobe and globus pallidum). Data was analysed with FSL, PETSurfer, and R.ResultSUVr20‐40 in the globus pallidum were generally higher than in the frontal lobe for all reference regions (p < 0.001). Frontal lobe, but not globus pallidum SUVr20‐40 were lower when derived from PERSI than when using the cerebellum (p < 0.001) or pons (p < 0.001). When comparing TACs, the PERSI ROI did not show the rapid peak (perfusion) as seen in all other regions (both target and reference), resulting in an overestimation of SUVR in the early phases of the scan. However, activity was the same for all reference regions at later timepoints resulting in similar SUVr20‐40 measures in the globus pallidum.ConclusionPERSI derived reference regions do not provide an accurate estimate of early phase perfusion and as such are not appropriate for use as reference regions for reference tissue modelling of PET data. Nonetheless, PERSI provides a suitable reference region for late phase SUVR calculations which is particularly relevant for diseases and tracers were an anatomically defined reference region cannot be easily determined. Future analyses on a larger cohort will determine effect sizes and ROCs to see whether PERSI is a suitable semi‐quantitative investigation to compare between different methodologies and diagnostic groups.

Exploring brain‐derived exosomal miRNA as a novel blood‐based biomarker for Alzheimer’s disease

AbstractBackgroundBiomarkers of Alzheimer’s disease (AD) currently rely on invasive and time‐consuming methods such as cerebrospinal fluid analysis and neuroimaging. Non‐invasive blood‐based biomarkers are needed for early diagnosis and risk prediction. Brain‐derived exosomes are promising candidates for blood biomarkers, as they can cross the blood‐brain barrier and enter the bloodstream, and exosomal miRNAs could serve as potential biomarkers for identifying patients with AD and screening individuals at high risk. Here, we conducted a hospital‐based case‐control study to investigate exosomal miRNAs as biomarkers for AD.MethodExosomes were isolated from plasma samples of AD patients (n = 18) and healthy controls (HC; n = 18), and exosomal miRNAs were analyzed using high‐throughput sequencing. AD‐differentially expressed miRNAs were identified and their associations with Mini‐Mental State Examination (MMSE) and amyloid positron emission tomography with standardized uptake value ratio (PET‐SUVR) were evaluated by Spearman’s correlation analyses. Logistic regression was used to select miRNA biomarkers and construct a classifier to discriminate AD from HC.ResultWe identified 65 miRNAs (47 downregulated and 18 upregulated) that were differentially expressed in AD, and 20 of which were significantly associated with both MMSE and PET‐SUVR (all false discovery rate adjusted P <0.05) (Figure 1). We selected hsa‐miR‐33a‐5p and hsa‐miR‐342‐3p as potential biomarkers to identify AD from HC, with an area under the curve (AUC) reaching 100% in the internal validation, whereas a model consisting of gender, age, and apolipoprotein E ε4 only had an AUC of 0.63. Both miRNAs were significantly correlated with MMSE and PET‐SUVR after adjusting age and sex. The miRNA target analysis revealed that the target genes of hsa‐miR‐33a‐5p and hsa‐miR‐342‐3p were involved in multiple processes related to the nervous system, including axon guidance, neurotransmitter receptors and postsynaptic signal transmission, and dopaminergic synapse, among others (Figure 2).ConclusionOur results suggest that differentially expressed miRNAs are significantly correlated with the pathological process of AD and cognitive function, and that blood exosomal miRNAs have the potential to detect AD with high sensitivity and specificity. These findings may provide a basis for the development of non‐invasive blood‐based biomarkers for AD diagnosis and risk prediction.