Comparison of Enlarged Perivascular Spaces in Early-Onset and Late-Onset Alzheimer Disease-related Cognitive Impairment: A Single Clinic-based Study in South Korea.

We examined whether there were differences in the presence of centrum semiovale-enlarged perivascular spaces (CSO-ePVS) and basal ganglia-ePVS (BG-ePVS) among patients with Alzheimer disease-related cognitive impairment (ADCI) based on their age of onset. Out of a total of 239 patients with cognitive impairment, 155 with positive amyloid-PET results were included. Among these, 43 had early-onset ADCI (EOADCI) and 112 had late-onset ADCI (LOADCI). Patients with LOADCI exhibited a higher prevalence of hypertension, lacunes, white matter hyperintensities, and BG-ePVS than those with EOADCI. BG-ePVS showed a significant correlation with age at the onset and the number of lacunes, whereas CSO-ePVS did not exhibit any association. The higher prevalence of BG-ePVS in patients with LOADCI might be attributable to vascular risk factors (hypertension) and cerebral small vessel disease (CSVD). These findings support the hypothesis that BG-ePVS is associated with CSVD and vascular risk factors, whereas CSO-ePVS is associated with cerebral amyloid angiopathy.

Assessing cognitive impairment and disability in older adults through the lens of whole brain white matter patterns.

INTRODUCTION: This study aimed to explore the potential of whole brain white matter patterns as novel neuroimaging biomarkers for assessing cognitive impairment and disability in older adults. METHODS: We conducted an in-depth analysis of magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) scans in 454 participants, focusing on white matter patterns and white matter inter-subject variability (WM-ISV). RESULTS: The white matter pattern ensemble model, combining MRI and amyloid PET, demonstrated a significantly higher classification performance for cognitive impairment and disability. Participants with Alzheimer's disease (AD) exhibited higher WM-ISV than participants with subjective cognitive decline, mild cognitive impairment, and vascular dementia. Furthermore, WM-ISV correlated significantly with blood-based biomarkers (such as glial fibrillary acidic protein and phosphorylated tau-217 [p-tau217]), and cognitive function and disability scores. DISCUSSION: Our results suggest that white matter pattern analysis has significant potential as an adjunct neuroimaging biomarker for clinical decision-making and determining cognitive impairment and disability. HIGHLIGHTS: The ensemble model combined both magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) and demonstrated a significantly higher classification performance for cognitive impairment and disability. Alzheimer's disease (AD) revealed a notably higher heterogeneity compared to that in subjective cognitive decline, mild cognitive impairment, or vascular dementia. White matter inter-subject variability (WM-ISV) was significantly correlated with blood-based biomarkers (glial fibrillary acidic protein and phosphorylated tau-217 [p-tau217]) and with the polygenic risk score for AD. White matter pattern analysis has significant potential as an adjunct neuroimaging biomarker for clinical decision-making processes and determining cognitive impairment and disability.

Emerging role of vascular burden in AT(N) classification in individuals with Alzheimer's and concomitant cerebrovascular burdens.

OBJECTIVES: Alzheimer's disease (AD) is characterised by amyloid-beta accumulation (A), tau aggregation (T) and neurodegeneration (N). Vascular (V) burden has been found concomitantly with AD pathology and has synergistic effects on cognitive decline with AD biomarkers. We determined whether cognitive trajectories of AT(N) categories differed according to vascular (V) burden. METHODS: We prospectively recruited 205 participants and classified them into groups based on the AT(N) system using neuroimaging markers. Abnormal V markers were identified based on the presence of severe white matter hyperintensities. RESULTS: In A+ category, compared with the frequency of Alzheimer's pathological change category (A+T-), the frequency of AD category (A+T+) was significantly lower in V+ group (31.8%) than in V- group (64.4%) (p=0.004). Each AT(N) biomarker was predictive of cognitive decline in the V+ group as well as in the V- group (p<0.001). Additionally, the V+ group showed more severe cognitive trajectories than the V- group in the non-Alzheimer's pathological changes (A-T+, A-N+; p=0.002) and Alzheimer's pathological changes (p<0.001) categories. CONCLUSION: The distribution and longitudinal outcomes of AT(N) system differed according to vascular burdens, suggesting the importance of incorporating a V biomarker into the AT(N) system.

Independent replication of advanced brain age in mild cognitive impairment and dementia: detection of future cognitive dysfunction.

We previously developed a novel machine-learning-based brain age model that was sensitive to amyloid. We aimed to independently validate it and to demonstrate its utility using independent clinical data. We recruited 650 participants from South Korean memory clinics to undergo magnetic resonance imaging and clinical assessments. We employed a pretrained brain age model that used data from an independent set of largely Caucasian individuals (n = 757) who had no or relatively low levels of amyloid as confirmed by positron emission tomography (PET). We investigated the association between brain age residual and cognitive decline. We found that our pretrained brain age model was able to reliably estimate brain age (mean absolute error = 5.68 years, r(650) = 0.47, age range = 49-89 year) in the sample with 71 participants with subjective cognitive decline (SCD), 375 with mild cognitive impairment (MCI), and 204 with dementia. Greater brain age was associated with greater amyloid and worse cognitive function [Odds Ratio, (95% Confidence Interval {CI}): 1.28 (1.06-1.55), p = 0.030 for amyloid PET positivity; 2.52 (1.76-3.61), p < 0.001 for dementia]. Baseline brain age residual was predictive of future cognitive worsening even after adjusting for apolipoprotein E e4 and amyloid status [Hazard Ratio, (95% CI): 1.94 (1.33-2.81), p = 0.001 for total 336 follow-up sample; 2.31 (1.44-3.71), p = 0.001 for 284 subsample with baseline Clinical Dementia Rating ≤ 0.5; 2.40 (1.43-4.03), p = 0.001 for 240 subsample with baseline SCD or MCI]. In independent data set, these results replicate our previous findings using this model, which was able to delineate significant differences in brain age according to the diagnostic stages of dementia as well as amyloid deposition status. Brain age models may offer benefits in discriminating and tracking cognitive impairment in older adults.

CaV α2δ Autoimmune Encephalitis: A Novel Antibody and its Characteristics.

OBJECTIVE: Discovery of a novel antibody would enable diagnosis and early treatment of autoimmune encephalitis. The aim was to discover a novel antibody targeting a synaptic receptor and characterize the pathogenic mechanism. METHOD: We screened for unknown antibodies in serum and cerebrospinal fluid samples from autoimmune encephalitis patients. Samples with reactivity to rat brain sections and no reactivity to conventional antibody tests underwent further processing for antibody discovery, using immunoprecipitation to primary neuronal cells, mass-spectrometry analysis, an antigen-binding assay on an antigen-overexpressing cell line, and an electrophysiological assay with cultured hippocampal neurons. RESULTS: Two patients had a novel antibody against CaV α2δ (voltage-gated calcium channel alpha-2/delta subunit). The patient samples stained neuropils of the hippocampus, basal ganglia, and cortex in rat brain sections and bound to a CaV α2δ-overexpressing cell line. Knockdown of CaV α2δ expression in cultured neurons turned off the immunoreactivity of the antibody from the patients to the neurons. The patients were associated with preceding meningitis or neuroendocrine carcinoma and responded to immunotherapy. In cultured neurons, the antibody reduced neurotransmitter release from presynaptic nerve terminals by interfering with tight coupling of calcium channels and exocytosis. INTERPRETATION: Here, we discovered a novel autoimmune encephalitis associated with anti-CaV α2δ antibody. Further analysis of the antibody in autoimmune encephalitis might promote early diagnosis and treatment. ANN NEUROL 2021;89:740-752.

Diagnostic Accuracy of Amyloid versus 18 F-Fluorodeoxyglucose Positron Emission Tomography in Autopsy-Confirmed Dementia.

OBJECTIVE: The purpose of this study was to compare the diagnostic accuracy of antemortem 11 C-Pittsburgh compound B (PIB) and 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) versus autopsy diagnosis in a heterogenous sample of patients. METHODS: One hundred one participants underwent PIB and FDG PET during life and neuropathological assessment. PET scans were visually interpreted by 3 raters blinded to clinical information. PIB PET was rated as positive or negative for cortical retention, whereas FDG scans were read as showing an Alzheimer disease (AD) or non-AD pattern. Neuropathological diagnoses were assigned using research criteria. Majority visual reads were compared to intermediate-high AD neuropathological change (ADNC). RESULTS: One hundred one participants were included (mean age = 67.2 years, 41 females, Mini-Mental State Examination = 21.9, PET-to-autopsy interval = 4.4 years). At autopsy, 32 patients showed primary AD, 56 showed non-AD neuropathology (primarily frontotemporal lobar degeneration [FTLD]), and 13 showed mixed AD/FTLD pathology. PIB showed higher sensitivity than FDG for detecting intermediate-high ADNC (96%, 95% confidence interval [CI] = 89-100% vs 80%, 95% CI = 68-92%, p = 0.02), but equivalent specificity (86%, 95% CI = 76-95% vs 84%, 95% CI = 74-93%, p = 0.80). In patients with congruent PIB and FDG reads (77/101), combined sensitivity was 97% (95% CI = 92-100%) and specificity was 98% (95% CI = 93-100%). Nine of 24 patients with incongruent reads were found to have co-occurrence of AD and non-AD pathologies. INTERPRETATION: In our sample enriched for younger onset cognitive impairment, PIB-PET had higher sensitivity than FDG-PET for intermediate-high ADNC, with similar specificity. When both modalities are congruent, sensitivity and specificity approach 100%, whereas mixed pathology should be considered when PIB and FDG are incongruent. ANN NEUROL 2021;89:389-401.

Longitudinal amyloid cognitive composite in preclinical Alzheimer's disease.

BACKGROUND AND PURPOSE: Previous studies have developed several cognitive composites in preclinical Alzheimer disease (AD). However, more sensitive measures to track cognitive changes and therapeutic efficacy in preclinical AD are needed considering the diverse sociocultural and linguistic backgrounds. This study developed a composite score that can sensitively detect the amyloid-ß (Aß)-related cognitive trajectory of preclinical AD using Korean data. METHODS: A total of 196 cognitively normal participants who underwent amyloid positron emission tomography were followed-up with neuropsychological assessments. We developed the Longitudinal Amyloid Cognitive Composite in Preclinical AD (LACPA) using the linear mixed-effects model (LMM) and z scores. The LMM was also used to investigate the longitudinal sensitivity of the LACPA and the association between time-varying brain atrophy and the LACPA. RESULTS: Considering the group-time interaction effects of each subtest, the Seoul Verbal Learning Test-Elderly version immediate recall/delayed recall/recognition, the Korean Trail Making Test B Time, and the Korean Mini-Mental State Examination were selected as components of the LACPA. The LACPA exhibited a significant group-time interaction effect between the Aß+ and Aß- groups (t = -3.288, p = 0.001). Associations between time-varying LACPA and brain atrophy were found in the bilateral medial temporal, right lateral parietal, and right lateral frontal regions, and hippocampal volume. CONCLUSIONS: The LACPA may contribute to reduction in time and financial burden when monitoring Aß-related cognitive decline and therapeutic efficacy of the disease-modifying agents specifically targeting Aß in secondary prevention trials.

Independent effects of amyloid and vascular markers on long-term functional outcomes: An 8-year longitudinal study of subcortical vascular cognitive impairment.

BACKGROUND AND PURPOSE: Subcortical vascular cognitive impairment (SVCI) is characterized by the presence of cerebral small vessel disease (CSVD) markers. Some SVCI patients also show Alzheimer's disease and cerebral amyloid angiopathy markers. However, the effects of these imaging markers on long-term clinical outcomes have not yet been established. The present study, therefore, aimed to determine how these imaging markers influence functional disability and/or mortality. METHODS: We recruited 194 participants with SVCI from the memory clinic and followed them up. All participants underwent brain magnetic resonance imaging at baseline, and 177 (91.2%) participants underwent beta-amyloid (Aß) positron emission tomography. We examined the occurrence of ischemic or hemorrhagic strokes. We also evaluated functional disability and mortality using the modified Rankin scale. To determine the effects of imaging markers on functional disability or mortality, we used Fine and Gray competing regression or Cox regression analysis. RESULTS: During a 8.6-year follow-up period, 46 of 194 patients (23.7%) experienced a stroke, 110 patients (56.7%) developed functional disabilities and 75 (38.6%) died. Aß positivity (subdistribution hazard ratio [SHR] = 2.73), greater white matter hyperintensity (WMH) volume (SHR = 3.11) and ≥3 microbleeds (SHR = 2.29) at baseline were independent predictors of functional disability regardless of the occurrence of stroke. Greater WMH volume (hazard ratio = 2.07) was an independent predictor of mortality. CONCLUSIONS: Our findings suggest that diverse imaging markers may predict long-term functional disability and mortality in patients with SVCI, which in turn may provide clinicians with a more insightful understanding of the long-term outcomes of SVCI.

Reduced brain subcortical volumes in patients with glaucoma: a pilot neuroimaging study using the region-of-interest-based approach.

BACKGROUND: While numerous neuroimaging studies have demonstrated that glaucoma is associated with smaller volumes of the visual cortices in the brain, only a few studies have linked glaucoma with brain structures beyond the visual cortices. Therefore, the objective of this study was to compare brain imaging markers and neuropsychological performance between individuals with and without glaucoma. METHODS: We identified 64 individuals with glaucoma and randomly selected 128 age-, sex-, and education level-matched individuals without glaucoma from a community-based cohort. The study participants underwent 3 T brain magnetic resonance imaging and neuropsychological assessment battery. Regional cortical thickness and subcortical volume were estimated from the brain images of the participants. We used a linear mixed model after adjusting for potential confounding variables. RESULTS: Cortical thickness in the occipital lobe was significantly smaller in individuals with glaucoma than in the matched individuals (ß = - 0.04 mm, P = 0.014). This did not remain significant after adjusting for cardiovascular risk factors (ß = - 0.02 mm, P = 0.67). Individuals with glaucoma had smaller volumes of the thalamus (ß = - 212.8 mm3, P = 0.028), caudate (ß = - 170.0 mm3, P = 0.029), putamen (ß = - 151.4 mm3, P = 0.051), pallidum (ß = - 103.6 mm3, P = 0.007), hippocampus (ß = - 141.4 mm3, P = 0.026), and amygdala (ß = - 87.9 mm3, P = 0.018) compared with those without glaucoma. Among neuropsychological battery tests, only the Stroop color reading test  score was significantly lower in individuals with glaucoma compared with those without glaucoma (ß = - 0.44, P = 0.038). CONCLUSIONS: We found that glaucoma was associated with smaller volumes of the thalamus, caudate, putamen, pallidum, amygdala, and hippocampus.

Interaction between Alzheimer's Disease and Cerebral Small Vessel Disease: A Review Focused on Neuroimaging Markers.

Alzheimer's disease (AD) is characterized by the presence of ß-amyloid (Aß) and tau, and subcortical vascular cognitive impairment (SVCI) is characterized by cerebral small vessel disease (CSVD). They are the most common causes of cognitive impairment in the elderly population. Concurrent CSVD burden is more commonly observed in AD-type dementia than in other neurodegenerative diseases. Recent developments in Aß and tau positron emission tomography (PET) have enabled the investigation of the relationship between AD biomarkers and CSVD in vivo. In this review, we focus on the interaction between AD and CSVD markers and the clinical effects of these two markers based on molecular imaging studies. First, we cover the frequency of AD imaging markers, including Aß and tau, in patients with SVCI. Second, we discuss the relationship between AD and CSVD markers and the potential distinct pathobiology of AD markers in SVCI compared to AD-type dementia. Next, we discuss the clinical effects of AD and CSVD markers in SVCI, and hemorrhagic markers in cerebral amyloid angiopathy. Finally, this review provides both the current challenges and future perspectives for SVCI.

Harmonisation of PET imaging features with different amyloid ligands using machine learning-based classifier.

PURPOSE: In this study, we used machine learning to develop a new method derived from a ligand-independent amyloid (Aß) positron emission tomography (PET) classifier to harmonise different Aß ligands. METHODS: We obtained 107 paired 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) PET images at the Samsung Medical Centre. To apply the method to FMM ligand, we transferred the previously developed FBB PET classifier to test similar features from the FMM PET images for application to FMM, which in turn developed a ligand-independent Aß PET classifier. We explored the concordance rates of our classifier in detecting cortical and striatal Aß positivity. We investigated the correlation of machine learning-based cortical tracer uptake (ML-CTU) values quantified by the classifier between FBB and FMM. RESULTS: This classifier achieved high classification accuracy (area under the curve = 0.958) even with different Aß PET ligands. In addition, the concordance rate of FBB and FMM using the classifier (87.5%) was good to excellent, which seemed to be higher than that in visual assessment (82.7%) and lower than that in standardised uptake value ratio cut-off categorisation (93.3%). FBB and FMM ML-CTU values were highly correlated with each other (R = 0.903). CONCLUSION: Our findings suggested that our novel classifier may harmonise FBB and FMM ligands in the clinical setting which in turn facilitate the biomarker-guided diagnosis and trials of anti-Aß treatment in the research field.

Strictly Lobar Microbleeds Reflect Amyloid Angiopathy Regardless of Cerebral and Cerebellar Compartments.

BACKGROUND AND PURPOSE: We aimed to determine whether lobar cerebellar microbleeds or concomitant lobar cerebellar and deep microbleeds, in the presence of lobar cerebral microbleeds, attribute to underlying advanced cerebral amyloid angiopathy pathology or hypertensive arteriopathy. METHODS: We categorized 71 patients with suspected cerebral amyloid angiopathy markers (regardless of the presence of deep and cerebellar microbleeds) into 4 groups according to microbleed distribution: L (strictly lobar cerebral, n=33), L/LCbll (strictly lobar cerebral and strictly lobar cerebellar microbleeds, n=13), L/Cbll/D (lobar, cerebellar, and deep microbleeds, n=17), and L/D (lobar and deep, n=8). We additionally categorized patients with cerebellar microbleeds into 2 groups according to dentate nucleus involvement: strictly lobar cerebellar (n=16) and dentate (n=14). We then compared clinical characteristics, Aß (amyloid-ß) positivity on PET (positron emission tomography), magnetic resonance imaging cerebral amyloid angiopathy markers, and cerebral small vessel disease burden among groups. RESULTS: The frequency of Aß positivity was higher in the L and L/LCbll groups (81.8% and 84.6%) than in the L/Cbll/D and L/D groups (37.5% and 29.4%; P<0.001), while lacune numbers were lower in the L and L/LCbll groups (1.7±3.3 and 1.7±2.6) than in the L/Cbll/D and L/D groups (8.0±10.3 and 13.4±17.7, P=0.001). The L/LCbll group had more lobar cerebral microbleeds than the L group (93.2±121.8 versus 38.0±40.8, P=0.047). The lobar cerebellar group had a higher Aß positivity (75% versus 28.6%, P=0.011) and lower lacune number (2.3±3.7 versus 8.6±1.2, P=0.041) than the dentate group. CONCLUSIONS: Strictly lobar cerebral and cerebellar microbleeds are related to cerebral amyloid angiopathy, whereas any combination of concurrent lobar and deep microbleeds suggest hypertensive angiopathy regardless of cerebral or cerebellar compartments.

Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes.

PURPOSE: We developed a machine learning-based classifier for in vivo amyloid positron emission tomography (PET) staging, quantified cortical uptake of the PET tracer by using a machine learning method, and investigated the impact of these amyloid PET parameters on clinical and structural outcomes. METHODS: A total of 337 18F-florbetaben PET scans obtained at Samsung Medical Center were assessed. We defined a feature vector representing the change in PET tracer uptake from grey to white matter. Using support vector machine (SVM) regression and SVM classification, we quantified the cortical uptake as predicted regional cortical tracer uptake (pRCTU) and categorised the scans as positive and negative. Positive scans were further classified into two stages according to the striatal uptake. We compared outcome parameters among stages and further assessed the association between the pRCTU and outcome variables. Finally, we performed path analysis to determine mediation effects between PET variables. RESULTS: The classification accuracy was 97.3% for cortical amyloid positivity and 91.1% for striatal positivity. The left frontal and precuneus/posterior cingulate regions, as well as the anterior portion of the striatum, were important in determination of stages. The clinical scores and magnetic resonance imaging parameters showed negative associations with PET stage. However, except for the hippocampal volume, most outcomes were associated with the stage through the complete mediation effect of pRCTU. CONCLUSION: Using a machine learning algorithm, we achieved high accuracy for in vivo amyloid PET staging. The in vivo amyloid stage was associated with cognitive function and cerebral atrophy mostly through the mediation effect of cortical amyloid.

Correction to: Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes.

Incorrect acknowledgments.

Correction to: Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes.

The Table 2 in the original version of this article contained a mistake in the alignment. Correct Table 2 presentation is presented here.

A new Centiloid method for 18F-florbetaben and 18F-flutemetamol PET without conversion to PiB.

PURPOSE: We developed a new method to directly calculate Centiloid (CL) units of 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) without conversion to the PiB standardized uptake value ratio (SUVR). METHODS: Paired FBB and FMM PET scans were obtained from 20 Alzheimer's disease-related cognitive impairment patients, 16 old controls, and 20 young controls. We investigated the correlations between the FBB and FMM CL units using the direct comparison of FBB-FMM CL (dcCL) method and the standard CL method and compare differences in FBB and FMM CL units between dcCL method and the standard method. RESULTS: Following the conversion of FBB or FMM SUVRs into CL units, a direct relationship was formed between the FBB or FMM SUVRs and the CL units using dcCL method (FBB dcCL = 151.42 × FBB dcSUVR - 142.24 and FMM dcCL = 148.52 × FMM dcSUVR - 137.09). The FBB and FMM CL units were highly correlated in both our method (R2 = 0.97, FMM dcCL = 0.97 × FBB dcCL + 1.64) and the standard method (R2 = 0.97, FMM CLstandard = 0.79 × FBB CLstandard + 1.36). However, the CL variations between FBB and FMM were smaller when calculated by dcCL method (6.15) than when calculated by the previous method (10.22; P = 0.01). CONCLUSIONS: Our findings suggest that our direct comparison of FBB-FMM method, rather than the standard method, is a reasonable way to convert FBB or FMM SUVRs into CL units, at least in environments where FBB or FMM ligands are used frequently.

Correction to: A new Centiloid method for 18F-florbetaben and 18F-flutemetamol PET without conversion to PiB.

Funding information from the original version of this article was incomplete. Complete information is presented here.

Application of an amyloid and tau classification system in subcortical vascular cognitive impairment patients.

OBJECTIVE: To apply an AT (Aß/tau) classification system to subcortical vascular cognitive impairment (SVCI) patients following recently developed biomarker-based criteria of Alzheimer's disease (AD), and to investigate its clinical significance. METHODS: We recruited 60 SVCI patients who underwent the neuropsychological tests, brain MRI, and 18F-florbetaben and 18F-AV1451 PET at baseline. As a control group, we further recruited 27 patients with AD cognitive impairment (ADCI; eight Aß PET-positive AD dementia and 19 amnestic mild cognitive impairment). ADCI and SVCI patients were classified as having normal or abnormal Aß (A-/A+) and tau (T-/T+) based on PET results. Across the three SVCI groups (A-, A+T-, and A+T+SVCI), we compared longitudinal changes in cognition, hippocampal volume (HV), and cortical thickness using linear mixed models. RESULTS: Among SVCI patients, 33 (55%), 20 (33.3%), and seven (11.7%) patients were A-, A+T-, and A+T+, respectively. The frequency of T+ was lower in A+SVCI (7/27, 25.9%) than in A+ADCI (14/20, 70.0%, p = 0.003) which suggested that cerebral small vessel disease affected cognitive impairments independently of A+. A+T-SVCI had steeper cognitive decline than A-SVCI. A+T+SVCI also showed steeper cognitive decline than A+T-SVCI. Also, A+T-SVCI had steeper decrease in HV than A-SVCI, while cortical thinning did not differ between the two groups. A+T+SVCI had greater global cortical thinning compared with A+T-SVCI, while declines in HV did not differ between the two groups. CONCLUSION: This study showed that the AT system successfully characterized SVCI patients, suggesting that the AT system may be usefully applied in a research framework for clinically diagnosed SVCI.

Functional connectivity associated with tau levels in ageing, Alzheimer's, and small vessel disease.

In Alzheimer's disease, tau pathology spreads hierarchically from the inferior temporal lobe throughout the cortex, ensuing cognitive decline and dementia. Similarly, circumscribed patterns of pathological tau have been observed in normal ageing and small vessel disease, suggesting a spatially ordered distribution of tau pathology across normal ageing and different diseases. In vitro findings suggest that pathological tau may spread 'prion-like' across neuronal connections in an activity-dependent manner. Supporting this notion, functional brain networks show a spatial correspondence to tau deposition patterns. However, it remains unclear whether higher network-connectivity facilitates tau propagation. To address this, we included 55 normal aged elderly (i.e. cognitively normal, amyloid-negative), 50 Alzheimer's disease patients (i.e. amyloid-positive) covering the preclinical to dementia spectrum, as well as 36 patients with pure (i.e. amyloid-negative) vascular cognitive impairment due to small vessel disease. All subjects were assessed with AV1451 tau-PET and resting-state functional MRI. Within each group, we computed atlas-based resting-state functional MRI functional connectivity across 400 regions of interest covering the entire neocortex. Using the same atlas, we also assessed within each group the covariance of tau-PET levels among the 400 regions of interest. We found that higher resting-state functional MRI assessed functional connectivity between any given region of interest pair was associated with higher covariance in tau-PET binding in corresponding regions of interest. This result was consistently found in normal ageing, Alzheimer's disease and vascular cognitive impairment. In particular, inferior temporal tau-hotspots, as defined by highest tau-PET uptake, showed high predictive value of tau-PET levels in functionally closely connected regions of interest. These associations between functional connectivity and tau-PET uptake were detected regardless of presence of dementia symptoms (mild cognitive impairment or dementia), amyloid deposition (as assessed by amyloid-PET) or small vessel disease. Our findings suggest that higher functional connectivity between brain regions is associated with shared tau-levels, supporting the view of prion-like tau spreading facilitated by neural activity.

Comparison of Amyloid ß and Tau Spread Models in Alzheimer's Disease.

Tau and amyloid ß (Aß), 2 key pathogenic proteins in Alzheimer's disease (AD), reportedly spread throughout the brain as the disease progresses. Models of how these pathogenic proteins spread from affected to unaffected areas had been proposed based on the observation that these proteins could transmit to other regions either through neural fibers (transneuronal spread model) or through extracellular space (local spread model). In this study, we modeled the spread of tau and Aß using a graph theoretical approach based on resting-state functional magnetic resonance imaging. We tested whether these models predict the distribution of tau and Aß in the brains of AD spectrum patients. To assess the models' performance, we calculated spatial correlation between the model-predicted map and the actual map from tau and amyloid positron emission tomography. The transneuronal spread model predicted the distribution of tau and Aß deposition with significantly higher accuracy than the local spread model. Compared with tau, the local spread model also predicted a comparable portion of Aß deposition. These findings provide evidence of transneuronal spread of AD pathogenic proteins in a large-scale brain network and furthermore suggest different contributions of spread models for tau and Aß in AD.