. Regional Aβ-tau interactions promote onset and acceleration of Alzheimer's disease tau spreading. Neuron. 2022 Jun 15;110(12):1932-1943.e5. Epub 2022 Apr 19 PubMed.


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  1. Regional Aβ-tau interactions promote onset and acceleration of Alzheimer’s disease tau spreading

    Alzheimer’s disease is characterized by cortical Aβ plaque pathology, followed by the successive spread of tau pathology throughout the brain, ensuing neurodegeneration and cognitive decline. Due to this cascade of pathological brain changes, AD is often considered an Aβ-induced tauopathy. Yet, the initial deposition patterns of Aβ and tau show a remarkable spatial mismatch (Jagust, 2018), which has posed challenges for developing a plausible mechanistic model of how Aβ may induce tau spreading in AD. In this elegant multimodal neuroimaging study jointly led by Bill Seeley and Joon-Kyung Seong, first author Wha Jin Lee proposes an integrative connectivity-based model that links cortical Aβ deposition with the successive spread of tau pathology throughout the brain.

    The authors suggest two critical events that give rise to the cortical spreading of tau pathology in AD: As a “first hit,” cortical Aβ may induce tau spreading from the entorhinal cortex to other temporal lobe regions via remote connections. As a “second hit,” the local convergence of Aβ and tau in the inferior temporal lobe may induce widespread and accelerated tau spreading to the rest of the brain which may then drive the development of neurodegeneration and cognitive decline in AD (La Joie et al., 2020Biel et al., 2021). 

    Together, the study is of high interest for the field, as the authors propose an integrated framework for how Aβ and tau pathology may interact i) remotely via neuronal connections as well as ii) locally to give rise to the brain-wide spreading pattern of tau pathology that is seen in AD.

    Yet, these findings also raise several further issues to be addressed in the future: First, it will be critical to determine the underlying molecular and cellular mechanisms by which cortical Aβ induces remote tau spreading from the entorhinal cortex. Some proposed hypotheses include changing neuronal activity levels in the target region (Busche et al., 2008), which may facilitate trans-synaptic tau propagation (Wu et al., 2016), or by changing biophysical properties of tau.

    These questions are difficult to address with neuroimaging methods alone, hence reverse translational studies using preclinical models will be necessary. Here, it will also be important to assess what differentiates local from remote Aβ and tau interactions, i.e., why local Aβ and tau interactions may potentiate and accelerate the subsequent spreading of tau pathology while remote Aβ vs. tau interactions have relatively circumscribed effects on tau spreading. It will be essential to understand the mechanisms that link Aβ and tau in order to identify potential novel treatment targets to “uncouple” Aβ deposition from tau spreading as early as possible in the course of AD.

    Second, it remains to be clarified how the proposed Aβ-centric tau spreading model aligns with the earliest expansion of tau along medial temporal lobe (MTL) subregions (Berron et al., 2021). Earliest tau is typically seen in the entorhinal cortex and subsequently in the hippocampus and other MTL regions (Berron et al., 2021). Yet, these MTL regions typically don’t harbor extensive Aβ plaque pathology, so it is unlikely that MTL Aβ attracts tau from the connected entorhinal cortex. Of note, tau deposition in the MTL is also observed in primary age-related tauopathy (PART) in the absence of Aβ (Crary et al., 2014), thus MTL tau spreading may be driven by an Aβ-independent mechanism that is not captured by the current model.

    Third, recent studies have emphasized considerable spatial heterogeneity in tau deposition and spreading in AD (Vogel et al., 2021; Franzmeier et al., 2020; Ossenkoppele et al., 2016), which does not adhere to the stereotypical “Braak-like” tau deposition pattern (Braak and Braak, 1991). Thus, it will be crucial to test whether the proposed model of remote and local Ab vs. tau interactions can also explain the heterogeneity in tau spreading which may ultimately give rise to the clinically heterogeneous manifestation of AD, including “atypical” AD phenotypes such as posterior cortical atrophy, dysexecutive, or language variant AD, all characterized by highly heterogeneous tau deposition patterns (Vogel et al., 2021; Ossenkoppele et al., 2016Graff-Radford et al., 2021). The UCSF cohort of atypical AD patients would have been a well-suited sample to test this question, hence we encourage the authors to validate their model in these patients in the future.

    As a limitation, Aβ levels saturate rather early in AD patients, and almost the entire neocortex harbors significant Aβ plaque pathology once patients show a positive Aβ-PET scan, regardless of the clinical phenotype (La Joie et al., 2019; Jeon et al., 2019; Beaufils et al., 2014). The ubiquity of Aβ can make it challenging to reliably test local or remote interactions with tau pathology, hence it should be clarified whether heterogeneity in Aβ deposition patterns may contribute to the heterogeneity in tau spreading (Vogel et al., 2021; Franzmeier et al., 2020; Ossenkoppele et al., 2016). Postmortem data suggest that initial sites of Aβ pathology vary (Thal et al., 2002), and sub-threshold Aβ has been shown to influence the degree of PET-assessed tau deposition in aging (Leal et al., 2018). Thus, it will be important to determine whether heterogeneous spatial deposition patterns of earliest “sub-threshold” Aβ (Leal et al., 2018) \may give rise to the subsequent heterogeneity in tau spreading patterns.

    Lastly, we would like to highlight the studies’ approach to stage patients according to their respective “phase” of tau spreading, which allows identifying individuals in pre-acceleration or acceleration phases. We believe that this staging system can be relevant for participant stratification in clinical trials, since removing Aβ in the tau pre-acceleration phase may be more efficient in preventing downstream tau spreading and therefore neurodegeneration and cognitive decline.

    Together, the current study is a major step forward in understanding the interaction between Aβ and tau pathology, which emphasizes the critical role of brain connectivity in the development of AD. The study also emphasizes the key role multi-modal neuroimaging can play to test and integrate mechanistic models of AD progression to better understand disease mechanisms. We believe this study will be an important starting point for future investigations to further disentangle the link between Aβ and tau pathology in AD by embedding both pathologies in a systems-level context of brain networks.


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  2. Both our paper and the Lee et al. paper tackle complementary questions relating to tau spread. While our study demonstrates that about 9 percent of clinically unimpaired individuals with abnormal Aβ have divergent cortical tau patterns, Lee et al. investigated the important question of how amyloid triggers tau spread when amyloid and tau follow different spatial patterns of spread, i.e., amyloid begins in the neocortex and spreads into the entorhinal cortex in Phase 2 (Thal et al., 2002), while tau begins in the transentorhinal region and spreads to entorhinal cortex and eventually neocortex (Braak et al., 2006).

    Lee et al. provide evidence for both remote and local amyloid-tau interactions, such that first there are remote interactions between cortical amyloid and entorhinal tau that promote tangle spread to nearby regions connected to entorhinal cortex, then tau deposits reach the inferior temporal gyrus where they locally interact with amyloid for the first time. Finally, tangles spread into amyloid-positive neocortical regions that are connected to the inferior temporal gyrus.

    That Lee et al. identify the inferior temporal gyrus as a key region for amyloid-tau interactions is intriguing, given that our study also suggested that inferior temporal, lateral parietal, and precuneus may be especially vulnerable areas for early tau deposition. Indeed, consistent with our suggestion, Figure 4B in the Lee et al. paper shows that lateral parietal and precuneus, in addition to inferior temporal gyrus, also show high local and remote amyloid and tau interactions. Together, our studies highlight the importance of these regions in models of Alzheimer’s disease progression.

    Lee et al. also noted that not all participants with early Alzheimer’s disease conformed to their spreading framework, highlighting the existence of heterogeneity across individuals. These non-conforming participants may indeed be the ones with divergent cortical tau patterns highlighted in our study. The same remote and local amyloid-tau interactions identified by Lee et al. may be occurring in those with divergent cortical tau, but the specific location of amyloid-tau interactions may vary outside of the entorhinal cortex, leading to the spatial heterogeneity that we and others (Vogel et al., 2021) have described.

    This model would provide an explanation for the known heterogeneity observed in various clinical presentations of Alzheimer’s disease (Ossenkoppele et al., 2016). In other words, the mechanisms related to local and remote amyloid-tau interactions could be similar across clinical presentations of Alzheimer’s disease, but individual differences in cortical hub regions could result in heterogeneous patterns of tau spread, which could in turn give rise to different clinical symptom profiles. The reasons why this heterogeneity occurs beyond entorhinal cortex remains unknown and is a key unanswered gap in Alzheimer’s disease research.


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    . Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer's disease. Brain. 2016 May;139(Pt 5):1551-67. Epub 2016 Mar 8 PubMed.

    View all comments by Christina Young
  3. These interesting papers deal with important questions regarding the spatial and temporal discrepancies between amyloid and tau that appeal to mechanisms that relate to the functional architecture of the brain. The authors are also trying to integrate the well-known phenomenon of phenotypic heterogeneity in clinical presentations, structural imaging, molecular imaging, functional imaging, and pathology (Graff-Radford et al., 2021).

    These are topics we have previously synthesized within the cascading network failure model of Alzheimer’s disease and subsequently investigated in aging and across AD phenotypes (Jones et al., 2017; Jones et al., 2016; Sintini et al., 2021Wiepert et al., 2017). In our studies, we use functional connectivity measures from patients rather than using templates from healthy controls, which allows for a different view of the functional physiology and its relationship to amyloid and tau reported in most studies. Our model emphasizes distributed functional physiology in ensembles of cells spanning large-scale anatomy associated with mental functions, or the global functional state space (GFSS) (Jones et al., 2022). In our study of the GFSS, we observed that there is a relatively simple relationship between mental functions and brain anatomy that can predict many aspects of Alzheimer’s physiology, including Braak staging of tau neurofibrillary tangle pathology. This underlying functional organization may drive many of the relationships reported in associative neuroimaging studies using large-scale anatomic patterns across all degenerative disease that cause dementia. That is why we were also able to relate this simple brain-behavior mapping to large-scale brain networks, mental task activation patterns, and a diverse array of clinical syndromes that span the dementia spectrum. In our model, large scale neurodynamics that take place across the landscape of the GFSS is the key element that links functional physiology to selective patterns of degenerative anatomy. In this model, neurodegenerative selectivity for certain dynamic brain patterns, or modes of function of the complex information processing system in the brain, requires a fundamental role for large-scale neurodynamic physiology in AD and related disorders.

    These functional dynamics that influence cellular activity across large ensembles of cells may contribute to homeostatic failure in protein processing physiology. This is consistent with an accelerated failure time model of amyloid and tau (Therneau et al., 2021). This alternative view of the relationship between amyloid, tau, and functional brain organization suggests different biomarkers and therapeutic targets related to large-scale functional physiology that can complement existing models focused more on molecular behavior.


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    View all comments by David Jones

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