Medicine (Austin & Northern Health) - Research Publications

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Head-to-Head comparison between Philips Gemini TF64 and Siemens Biograph Vision 600 for brain amyloid Centiloid quantitation

Li, S ; Bourgeat, P ; Bozinovski, S ; Huang, K ; Guzman, R ; Williams, R ; Fripp, J ; Villemagne, VL ; Rowe, C ; Dore, V (Wiley, 2022-12-01)

Abstract Background The Centiloid (CL) scale calibrates the beta‐amyloid (Aβ) deposition from different PET tracers to a standardised 0‐100 CL unit scale. As imaging sites update their PET cameras, most are switching to digital detector systems with superior resolution and sensitivity that may affect quantitation. This has significant implications for dementia clinical trials. In this study, we examine the impact on CL quantification between Philips Gemini TF64 and Siemens Biograph Vision 600. Method Seven subjects (76.4±2.2 yo) were imaged with 18F‐NAV4694 on both Gemini TF64 and Biograph Vision consecutively with an average scan interval of 25.1±11.2 weeks. The injected doses were 200MBq and 100MBq, respectively. On the Gemini TF64, the PET images were reconstructed by LOR‐RAMLA algorithm with smoothing parameter setup as ‘SHARP’. On Biograph Vision, the PET images were reconstructed by OSEM‐3D (8 iterations and 5 subsets, TOF enabled) with 3mm post Gaussian smoothing. A T1 MRI image was acquired for each subject. As per the standard Centiloid method the whole cerebellum was used as the reference in SUVR images, and all images were processed using CapAIBL to calculate the CL using both MR‐based and MR‐Less spatial normalisation. Result Figure 1 shows the CL images of a subject scanned on Gemini TF64 and Biograph Vision within sixteen weeks. The Biograph Vision images have higher contrast and higher spatial resolution despite using half of the dose. Figure 2 shows the linear regression plot of the scanner comparison. Biograph Vision CL are progressively higher than those obtained from the Gemini TF64 as the CL value rises (Table 1). There were no significant differences between the MR‐based and MR‐less results. Conclusion Biograph Vision yields higher SUVR and therefore CL values compared to Gemini TF64 in a head‐to‐head comparison. These results show that the selection of PET camera has a significant impact on CL quantification, which needs to be considered when merging cohorts from different studies or changing cameras during longitudinal studies or trials. These initial results indicate that the CL difference could be corrected by a linear transform.
CenTauRz: A standardized quantification of tau PET scans

Dore, V ; Bullich, S ; Bohorquez, SS ; Leuzy, A ; Shimada, H ; Rowe, C ; Bourgeat, P ; Lopresti, BJ ; Huang, K ; Krishnadas, N ; Fripp, J ; Takado, Y ; Stephens, AW ; Weimer, R ; Higuchi, M ; Hansson, O ; Villemagne, VL (Wiley, 2022-12-01)

Background: Over the past decade, several PET tracers were developed to visualise and quantify tau pathology in vivo. However, all these tracers have distinct off-target binding, different dynamic ranges and likely different levels of non-specific binding resulting in large variability in semiquantification. We propose to standardise the sampling and the quantification across all available tau tracers. Method: 549 participants underwent tau scans with either 18F-FTP (Cognitively Unimpaired (CU)=54/AD=14), 18F-MK6240 (CU=186/AD=89), 18F-PI2620 (CU=17/AD=21), 18F-PM-PBB3 (CU=30/AD=28), 18F-GTP1 (CU=7/AD=38) or 18F-RO948 (CU=35/AD=30). All CU individuals were Aβ- and all AD were Aβ+. The tau scans were spatially normalized using CapAIBL and the cerebellar cortex was used as reference region. We constructed a “universal” tau mask from the intersection of all the specific tau tracer masks, after subtracting AD from CU. All tau PET studies were sampled with a Mesial Temporal (MTL) and a Meta Temporal (MetaT) composites constrained by the universal mask. For each tracer and in composite, the mean and standard deviation of the Aβ- CU SUVR for each tau tracer were used to generate z-scores (CenTauRz). Result: Using a threshold of 2 CenTauRz in the MetaT regions, all tracers highly discriminated Aβ+ AD from Aβ- CU (ACC=[0.94-1], sens=[0.84-1], spec=[0.96-1]) with mean CenTauRz for the different AD cohorts ranging from 8 to 14. Lower accuracy was observed in the MTL (ACC=[0.78-1]) due to lower sensitivity in some cohorts [0.65-1] however, the specificity was similar to that in the MetaT composite (spec=[0.94,1]). Conclusion: All tracers exhibited comparably high discriminative power to separate Aβ+ AD from Aβ- CU, where AD Aβ+ displayed a consistent range of CenTauRz across tracers. However, there were some differences between cohorts. For example, different PET scanners, with different sensitivities were used. For some cohorts, scans were selected as extreme representative cases, while for others the scans were more representative of clinical settings, with AD patients at early stages (with low or negative tau scans) or with suspected hippocampal sparing subtype that likely explains the lower accuracy in the MTL for some cohorts. Further studies with larger cohorts to validate the universal mask and CenTauRz scale are ongoing.
Prevalence and Associations of Frailty in Preclinical Alzheimer’s Disease

Lee, K ; Huynh, A ; Moe, T ; Amadoru, S ; Zisis, G ; Raman, R ; Aisen, P ; Ernstrom, K ; Sperling, RA ; Masters, CL ; Yates, PA (Wiley, 2022-12)

Background The prevalence of frailty in asymptomatic Alzheimer’s disease is not clear. In this cohort screened for a preclinical Alzheimer’s disease clinical trial, we aimed to compare the prevalence of frailty between participants with and without elevated amyloid as determined by PET and determine if frailty influences the relationship between amyloid and cognition. Method Analysis of pre‐randomization data from the Anti‐Amyloid Treatment in Asymptomatic Alzheimer’s (A4) Study, a clinical prevention trial of an anti‐amyloid monoclonal antibody in individuals who were cognitively normal with elevated amyloid burden, to determine a cumulative‐deficits Frailty Index (FI). Logistic regression was used to investigate the difference in the prevalence of frailty, defined as a FI greater than 0.25, according to amyloid status (Aβ+/‐), adjusted for age, gender and education. ANCOVA was used to examine the influence of frailty on the relationship between amyloid status and cognition (Preclinical Alzheimer Cognitive Composite [PACC] score), including an interaction term of frailty and amyloid, adjusted for age, gender and education. Result 4,486 participants were included (mean age 71.3±4.7 years, 1323 participants Aβ+(29.5%), 59.4% female). Adjusting for age, sex and education, Aβ+ participants were 1.48 times more likely to be frail compared to Aβ− (p<0.001). Frail participants had a lower PACC score compared to non‐frail participants (p<0.001). Both frailty and amyloid status were associated with poorer cognition after adjusting for age, sex and education (p<0.001), but frailty did not influence the relationship between cognition and amyloid status. Conclusion There is strong evidence that elevated amyloid burden was associated with an increased risk of frailty and that frailty reduced cognitive performance compared to non‐frail participants in this cohort screened for a preclinical AD trial. These relationships, the potential underlying mechanisms, and whether this may be applicable to longitudinal outcomes, warrant further study.
Cross‐sectional and longitudinal comparison of 18F‐MK6240 and 18F‐Flortaucipir in populations matched for centiloid, age and MMSE

Bourgeat, P ; Krishnadas, N ; Dore, V ; Mulligan, RS ; Tyrrell, R ; Bozinovski, S ; Huang, K ; Lamb, F ; Fripp, J ; Villemagne, VL ; Rowe, C (Wiley Open Access, 2022-12)

Background Longitudinal tau quantification may provide a useful outcome measure in disease‐specific therapeutic trials. Different tau PET tracers may have different sensitivity to longitudinal changes, but without a head‐to‐head comparison, equating results from different cohorts using different tracers can be biased. In this study, we aim to minimise this bias by matching participants in two cohorts imaged using 18F‐MK6240 and 18F‐Flortaucipir (FTP). Method A subset of 93 participants from AIBL and 93 from ADNI, imaged at baseline and 1 year later using 18F‐MK6240 and 18F‐FTP, respectively, were matched based on baseline clinical diagnosis, MMSE, age, and Centiloid value (CL). PET images were analysed with CapAIBL. Amyloid positivity (+/‐) was defined based on a threshold of 25CL. Subjects were grouped as 34 cognitively unimpaired amyloid negative (CU‐) and 24 positive (CU+), 18 mild cognitive impairment positive (MCI+) and 17 Alzheimer’s disease positive (AD+). Tracer retention was measured in the mesial temporal (Me), meta‐temporal (MT), temporoparietal (Te) and rest of the cortex (R). T‐tests were employed to assess group separation at baseline using SUVR and longitudinally using SUVR/Yr. Result As per selection criteria, there were no significant differences in age, MMSE or Centiloid between the cohorts using 18F‐MK6240 or 18F‐FTP in each subgroups. Baseline SUVR were significantly different between CU‐/CU+, CU+/MCI+ and CU+/AD+ in all regions for both tracers, except for CU‐/CU+ in R for 18F‐MK6240 (Figure 1). Using 18F‐MK6240, rate of change in CU+ was significantly higher than CU‐ in MT and Te, and both MCI+ and AD+ were higher than CU+ in R (Figure 2.Left). Using 18F‐FTP, rate of change in MCI+ was significantly higher than CU+ in Te, and AD+ higher than CU+ in MT, Te and R (Figure 2.Right). Conclusion In our matched cohorts using 18F‐MK6240 or 18F‐FTP, we found that, at baseline, both tracers can detect significant differences between clinical groups. However, 18F‐MK6240 was able to detect higher rates of accumulation at preclinical stages (CU+). These results in well‐matched cohorts indicate that 18F‐MK6240 might be a more sensitive tracer to detect early accumulation. Longitudinal head‐to‐head comparison will be required to confirm these results.
Objectively measured physical activity and cognition in cognitively normal older adults: A longitudinal analysis of the Australian Imaging Biomarkers and Lifestyle (AIBL) study

Sewell, KR ; Rainey‐Smith, S ; Villemagne, VL ; Peiffer, JJ ; Sohrabi, HR ; Taddei, K ; Ames, D ; Maruff, P ; Laws, SM ; Masters, CL ; Rowe, C ; Martins, RN ; Erickson, KI ; Brown, BM (Wiley Open Access, 2022-12)

Background Physical inactivity is one of the greatest modifiable risk factors for dementia and research shows physical activity can delay cognitive decline in older adults. However, much of this research has used subjective physical activity data and a single follow‐up cognitive assessment. Further studies using objectively measured physical activity and comprehensive cognitive data measured at multiple timepoints are required. Methods Participants were 199 community‐dwelling cognitively normal older adults (68.7 5.9 years) from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. Actigraphy was used to measure physical activity at baseline, yielding measures of intensity (peak counts), total activity (total counts) and energy expenditure (kilocalories; k/cal). Cognitive function was assessed using a cognitive battery administered every 18‐months from baseline (3‐11 years follow‐up), yielding composite scores for episodic memory, executive function, attention and processing speed, and global cognition. Results Higher baseline energy expenditure predicted improvements in episodic memory and maintained global cognition over time (β = 0.011, SE = 0.005, p = 0.031; β = 0.009, SE = 0.004, p = 0.047, respectively). Both physical activity intensity and total activity predicted global cognition, such that those with higher peak and total counts had better cognition over time (β = 0.012, SE = 0.004, p = 0.005; β = 0.012, SE = 0.004, p = 0.005, respectively). Finally, higher total activity predicted improved episodic memory over time (β = 0.011, SE = 0.005, p = .022). Conclusion These results suggest that physical activity is associated with preserved cognitive function over time, and that activity intensity may play an important role. This research further highlights the importance of early intervention to prevent cognitive decline and may aid in informing lifestyle interventions for dementia prevention.
Plasma glial fibrillary acidic protein is associated with reactive astrogliosis assessed via 18F-SMBT-1 PET

Chatterjee, P ; Dore, V ; Pedrini, S ; Krishnadas, N ; Thota, RN ; Bourgeat, P ; Rainey‐Smith, S ; Burnham, SC ; Fowler, C ; Taddei, K ; Mulligan, RS ; Ames, D ; Masters, CL ; Fripp, J ; Rowe, C ; Martins, RN ; Villemagne, VL (Wiley, 2022-12)

Background Reactive astrogliosis is an early event along the Alzheimer’s disease (AD) continuum. We have shown that plasma glial fibrillary acidic protein (GFAP), reflecting reactive astrogliosis, is elevated in cognitively unimpaired individuals with preclinical AD (Chatterjee et al., 2021). We reported similar findings using 18F‐SMBT‐1, a PET tracer for monoamine oxidase B (MAO‐B) (Villemagne et al., 2022). To provide further evidence of their relationship with reactive astrogliosis we investigated the association between GFAP and 18F‐SMBT‐1 in the same participants. Method Plasma GFAP, Aβ42 and Aβ40 levels were measured using the Single Molecule Array platform in 71 participants comprising 54 healthy controls (12 Aβ+ and 42 Aβ‐), 11 MCI(3 Aβ+ and 8 Aβ‐) and 6 probable AD(5 Aβ+ and 1 Aβ‐) patients from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing cohort. These participants also underwent 18F‐SMBT‐1 and Aβ PET imaging. Aβ imaging results were expressed in Centiloids (CL; ≥20 CL classified as Aβ+). 18F‐SMBT‐1 Standard Uptake Value Ratio (SUVR) were generated using the subcortical white matter as reference region. Linear regression analyses were carried out using plasma GFAP levels as the dependent variable and regional 18F‐SMBT‐1 SUVR as the independent variable, before and after adjusting for age, sex, soluble Aβ (plasma Aβ1‐42/Aβ1‐40 ratio) and insoluble Aβ (Aβ PET). Result Plasma GFAP was significantly associated with 18F‐SMBT‐1 SUVR in brain regions of early Aβ deposition, such as the supramarginal gyrus (SG, β=.361, p=.002), posterior cingulate (PC, β=.308, p=.009), lateral temporal (LT, β=.299, p=.011), lateral occipital (LO, β=.313, p=.008) before adjusting for any covariates. After adjusting for covariates age, sex and soluble Aβ, GFAP was significantly associated with 18F‐SMBT‐1 PET signal in the SG (β=.333, p<.001), PC (β=.278, p=.005), LT (β=.256, p=.009), LO (β=.296, p=.004) and superior parietal (SP, β=.243, p=.016). On adjusting for age, sex and insoluble Aβ, GFAP was significantly associated with SMBT‐1 PET in the SG (β=.211, p=.037) however only a trend towards significance was observed in the PC (β=.186, p=.052) and LT (β=.171, p=.067) (Figure 1). Conclusion There is an association between plasma GFAP and regional SMBT‐1 PET that is primarily driven by brain Aβ load.
Alzheimer’s disease specific MRI brain regions are differentially associated with accelerated decline as defined using sigmoidal cognitive turning point methodology in amyloid‐positive AIBL participants

Gillis, C ; Cespedes, MI ; Maserejian, NN ; Dore, V ; Maruff, P ; Fowler, C ; Rainey‐Smith, S ; Villemagne, VL ; Rowe, C ; Martins, RN ; Vacher, M ; Masters, CL ; Doecke, JD (Wiley, 2022-12)

Background Variability in cognitive decline among adults with Alzheimer’s disease (AD) is seen across studies. While such variability is often modelled using linear models, in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study, application of a sigmoidal methodology has shown excellent precision in modelling cognitive and biomarker changes. Here we expand these findings by examining associations of brain volumes in AD specific Regions of Interest (ROIs) with accelerated cognitive decline among amyloid‐beta positive (Ab+) AIBL participants. Method Longitudinal cognitive scores for the AIBL PACC, Language, Visuospatial functioning and CDR‐SB were mapped to sigmoidal trajectories, with a threshold defining the inflection point of accelerated cognitive decline. Participants to the left of the threshold were classified as having non‐accelerated decline (non‐accelerators), and participants beyond the threshold were classed as accelerators (Figure 1B). Using these classifications, we investigated differences in 16 ICV corrected ROI (left and right hemispheres pooled) for reductions in brain volume via generalised linear models adjusted for age, gender, and APOE‐e4 status. Three participant subgroups were tested: 1) Ab+/Tau unknown, 2) Ab+/Tau‐ and 3) Ab+/Tau+. Significant t‐values for the summed ROI volumes were mapped on a standard brain mesh for visualisation. Result Of regions tested, two stood out consistently amongst top markers in each of the participant subgroups and cognitive outcomes: 1) supramarginal volume and 2) middle temporal volume (Figure 1C). Largest volume differences between accelerators and non‐accelerators were seen in the Ab+/Tau+ group; whilst smallest p‐values were in the Ab+/Tau unknown group due to a larger sample size (Table 1). Brain mesh visualization showed most of the AD signature ROIs altered in accelerator groups as compared with non‐accelerator groups. Figure 1D shows the AD signature for each cognitive outcome amongst the Ab+/Tau participant group. Top ranked ROI for the left being middle temporal volume (T=7.10, PACC) and supramarginal volume (T=7.10, CDR‐SB). Conclusion Sigmoid analyses of MRI using binary cognitive scores show decreased ROI volumes in AIBL Ab+ participants with accelerated cognitive decline. This effect was mediated by known information on Tauopathy. Whilst effect sizes were high, smaller sample sizes in some groups affected p‐values and should therefore be replicated in larger samples.
Comparing the longitudinal progression of CSF biomarkers with PET Amyloid biomarkers for Alzheimer’s disease

Cox, T ; Bourgeat, P ; Dore, V ; Doecke, JD ; Fripp, J ; Chatterjee, P ; Schindler, EE ; Benzinger, TLS ; Rowe, C ; Villemagne, VL ; Weiner, MW ; Morris, JC ; Masters, CL (Wiley, 2022-12)

Background Cerebrospinal fluid (CSF) soluble biomarkers are useful at detecting pre‐clinical levels of Alzheimer’s disease (AD) biomarkers of b‐amyloid (Ab) and tau. Disease progression times for participants in longitudinal studies can be estimated for different biomarkers. Utilizing a new technique, this work compared the disease progression times between CSF and PET biomarkers. Methods Four hundred and ten participants from the Alzheimer’s Dementia Onset and Progression in International Cohorts (ADOPIC) including participants form ACS/OASIS, ADNI and AIBL with three or more data points of longitudinal CSF Ab42 and pTau181 (pTau) and Ab PET were selected. PET results were expressed in Centiloid (CL), (299 cognitively unimpaired, 107 mild cognitively impaired, 4 AD dementia; aged 69±9; 216 females (NAIBL=30, NADNI=252, NOASIS=128). Disease trajectory curves for individual biomarkers and the pTau/Ab42 ratio were created by: 1) Fitting a function to the rates of change of the variable of interest versus its mean value), 2) integrating the fit to obtain longitudinal trajectory curves as a function of disease progression time for each of the variables. The participants’ disease progression time along each curve were estimated. Threshold values for Ab PET and pTau/Ab42 ratios were calculated using a gaussian mixture model. Estimates of age of onset were calculated using the progression times. The participants’ disease progression times for each of the different variables were compared using rank correlations. Results Rank correlations for the progression times were: r(Ab42, Ab PET) = 0.75, r(pTau, Ab PET)=0.62, and r(pTau/Ab42, Ab PET)=0.83. The estimated ages at which participants’ reach Ab PET and the pTau/Ab42 ratio thresholds are compared in Fig 1, the average age at which were estimated to reach the threshold values were 55 yr for pTau/Ab42 (threshold of 0.021) and 61 yr for Ab PET (threshold of 22 CL). Conclusions The high correlation between pTau/Ab42 and Ab PET, indicates that pTau/Ab42 captures the progression of AD pathology better than the individual CSF biomarkers. On average participants’ reach abnormal levels of pTau/Ab42 earlier than Ab PET. Further work is required to understand individual variations in progression times.
Altered levels of plasma kallikrein‐7 in prodromal Alzheimer’s disease

Roberts, BR ; Roberts, AM ; Cortes, L ; Fowler, C ; Villemagne, VL ; Masters, CL ; Ryan, TM (Wiley, 2022-12)

Background The accumulation of Aβ is thought to be dependent on imbalances in production or clearance of the peptide. At the core of the production and the breakdown of Aβ are changes in the activity of proteases. Several studies have suggested that the level and activity of brain proteases involved in the clearance pathway are perturbed in the disease. Given that AD is essentially a protease disease, we sought to determine if key proteases were altered in the blood plasma fraction, reflecting changes that are occur in the brain. Method We used a fast commercial tool to investigate the level of 34 proteases in plasma between healthy controls and AD patients. Next we used an ELISA and western blot assays to validate results from the discovery assay. Result We discovered that the protease kallikrein‐7 (KLK7) was significantly elevated in AD plasma. We then asked how early in the disease time course is KLK7 elevated. We used plasma from cognitively normal cases (n=120) with either high or low levels of brain amyloid based on PET imaging and found that KLK7 leaves are decreased. Conclusion These results suggest that the plasma level of Kallikrein‐7 may be an important tool in detecting elevated levels of brain amyloid before symptoms of AD become apparent.
Subcortical tau‐accumulation predicts neuronal dysfunction in the cortex based on functional connectivity in 4R‐tauopathies

Roemer, SN ; Franzmeier, N ; Katzdobler, S ; Nitschmann, A ; Barthel, H ; Bischof, GN ; Beyer, L ; Marek, K ; Song, M ; Wagemann, O ; Palleis, C ; Nack, A ; Fietzek, U ; Kurz, C ; Haeckert, J ; Stapf, T ; Ferschmann, C ; Scheifele, M ; Eckenweber, F ; Biechele, G ; Biel, D ; Dewenter, A ; Steward, A ; Schoenecker, S ; Saur, D ; Schroeter, ML ; Rumpf, J ; Rullmann, M ; Schildan, A ; Patt, M ; Stephens, AW ; van Eimeren, T ; Drzezga, A ; Danek, A ; Classen, J ; Bürger, K ; Janowitz, D ; Rauchmann, B ; Stöcklein, S ; Perneczky, R ; Schoeberl, F ; Zwergal, A ; Bartenstein, P ; Neumaier, B ; Villemagne, VL ; Seibyl, J ; Sabri, O ; Levin, J ; Brendel, M ; Höglinger, G (Wiley, 2022-12)

Abstract Background 4‐repeat (4R) tauopathies are neurodegenerative diseases characterized by cerebral accumulation of 4R tau pathology. The most prominent 4R‐tauopathies are progressive‐supranuclear‐palsy (PSP) and corticobasal‐syndrome (CBS) characterized by tau accumulation in subcortical nuclei as well as cortical neuronal dysfunction, as shown by PET‐assessed hypoperfusion and glucose hypometabolism. Yet, there is a spatial mismatch between subcortical tau deposition patterns and cortical neuronal dysfunction and it is unclear how these two pathological brain changes are interrelated. Here, we hypothesized that subcortical tau pathology induces diaschisis‐like neuronal dysfunction in functionally connected cortical regions. Method We included 47 patients with clinically diagnosed PSP or CBS who underwent structural MRI and 18F‐PI‐2620 tau‐PET. PI‐2620 PET was recorded using a dynamic one‐shot, two‐stop acquisition protocol, to determine an early 0.5‐2.5min post‐tracer‐injection perfusion window for assessing cortical neuroinjury in 200 cortical ROIs of the Schaefer atlas, as well as a 20‐40min post‐tracer‐injection window to determine 4R‐tau load in 32 subcortical ROIs of the TIAN atlas. We determined tau epicenters as 10% of subcortical ROIs with highest tau‐PET, and assessed the connectivity of tau epicenters to cortical ROIs using an age‐matched 3T resting‐state fMRI template derived from 69 healthy elderly. Using linear regression, we assessed whether i) higher subcortical tau‐PET was associated with overall reduced cortical perfusion and ii) whether cortical hypoperfusion was observed preferentially in regions closely connected to subcortical tau epicenters. Result As hypothesized, higher subcortical tau‐PET was associated with lower cortical perfusion (R=‐0,37, p‐value: <0,011, Fig.1). Using group‐average tau‐PET and perfusion‐PET, we found that the seed‐based connectivity pattern of subcortical tau epicenters predicted cortical perfusion patterns, where cortical regions that were more closely connected to the tau epicenter showed stronger hypoperfusion (R=‐0,16, p‐value: <0,023, Fig.2A). This association was also observed on the subject level, as indicated by overall negative b‐values of the association between tau epicenter connectivity and cortical perfusion (one‐sample t‐test: t‐value: ‐3,45, p‐value: <0,001, Fig.3). Conclusion In 4R‐tauopathies subcortical tau‐accumulation is associated with remote neuronal dysfunction in functionally connected cortical regions. This suggests that subcortical tau pathology may induce diaschisis‐like cortical dysfunction, which may contribute to clinical disease manifestation and clinical heterogeneity.

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