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

Lee, Wha Jin; Brown, Jesse A.; Kim, Hye Ryun; Joie, Renaud La; Cho, Hanna; Lyoo, Chul Hyoung; Rabinovici, Gil D.; Seong, Joon‐Kyung; Seeley, William W.

doi:10.1016/j.neuron.2022.03.034

Cited by 110 publications

(76 citation statements)

References 59 publications

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“…Note that the regions of maximal tau aggregation across AD phenotypes represent cross-sectional group-level maxima and should not be interpreted as the focal origin of pathology based on our data. Instead, tau accumulation that has spread outside of vulnerable medial temporal regions can undergo local interactions with amyloid-β in the neocortex, resulting in increased rates of tau accumulation and spread across different brain networks ( 52 ). We hypothesize that differences in connectivity-based vulnerability may underlie the diversity of clinical phenotypes in AD.…”

Section: Discussionmentioning

confidence: 99%

Intrinsic connectivity of the human brain provides scaffold for tau aggregation in clinical variants of Alzheimer’s disease

et al. 2022

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Alzheimer’s disease (AD) phenotypes might result from differences in selective vulnerability. Evidence from preclinical models suggests that tau pathology has cell-to-cell propagation properties. Therefore, here, we tested the cell-to-cell propagation framework in the amnestic, visuospatial, language, and behavioral/dysexecutive phenotypes of AD. We report that each AD phenotype is associated with a distinct network-specific pattern of tau aggregation, where tau aggregation is concentrated in brain network hubs. In all AD phenotypes, regional tau load could be predicted by connectivity patterns of the human brain. Furthermore, regions with greater connectivity displayed similar rates of longitudinal tau accumulation in an independent cohort. Connectivity-based tau deposition was not restricted to a specific vulnerable network but was rather a general property of brain organization, linking selective vulnerability and transneuronal spreading models of neurodegeneration. Together, this study indicates that intrinsic brain connectivity provides a framework for tau aggregation across diverse phenotypic manifestations of AD.

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Section: Discussionmentioning

confidence: 99%

Intrinsic connectivity of the human brain provides scaffold for tau aggregation in clinical variants of Alzheimer’s disease

et al. 2022

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“…A flow graph is a transformation of a network’s (possibly sparse) connectivity matrix, W ij , into a fully-weighted matrix in which the dynamics of a Markov process are embedded into edge weights [75]. Flow graphs have been applied in neuroscience for the purposes of community detection [76] and for tracking the propagation of tau deposition [77]. For a continuous time random walk with dynamics , the corresponding flow graph is given by W l ( t ) ij = ( e − tL ) ij s j .…”

Section: Methodsmentioning

confidence: 99%

Multi-policy models of interregional communication in the human connectome

Betzel

Faskowitz

Mišić

et al. 2022

Preprint

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Network models of communication, e.g. shortest paths, diffusion, navigation, have become useful tools for studying structure-function relationships in the brain. These models generate estimates of communication efficiency between all pairs of brain regions, which can then be linked to the correlation structure of recorded activity, i.e. functional connectivity (FC). At present, however, communication models have a number of limitations, including difficulty adjudicating between models and the absence of a generic framework for modeling multiple interacting communication policies at the regional level. Here, we present a framework that allows us to incorporate multiple region-specific policies and fit them to empirical estimates of FC. Briefly, we show that many communication policies, including shortest paths and greedy navigation, can be modeled as biased random walks, enabling these policies to be incorporated into the same multi-policy communication model alongside unbiased processes, e.g. diffusion. We show that these multi-policy models outperform existing communication measures while yielding neurobiologically interpretable regional preferences. Further, we show that these models explain the majority of variance in time-varying patterns of FC. Collectively, our framework represents an advance in network-based communication models and establishes a strong link between these patterns and FC. Our findings open up many new avenues for future inquiries and present a flexible framework for modeling anatomically-constrained communication.

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“…However, Lee and colleagues [22] observed a signi cant local interaction of Aβ plaques and tau tangles in the inferior temporal region, sparking the most signi cant acceleration of tauopathy. Modeling methodology differences might underlie the discrepancies.…”

Section: Discussionmentioning

confidence: 99%

“…Modeling methodology differences might underlie the discrepancies. While the local Aβ and tau interaction (Aβ × tau) within the same region was modeled in their study [22], we used a summary cortical SUVR of several areas [31] to represent cortical Aβ burden, and Aβ plaques and regional tau tangles were modeled separately to distinguish their respective contributions in the downstream tau accumulations. Therefore, our ndings suggest that tau propagations depend more on existing tau tangles than cortical Aβ plaques in late tau-affected ROIs of Temporal-metaROI when Aβ plaques and tau tangles are already widespread in neocortex and Temporal-metaROI.…”

Section: Discussionmentioning

confidence: 99%

“…Importantly, aberrant neocortical tau aggregations rather than Aβ plaques are more strongly associated with neurodegeneration and cognitive decline in AD [5,6,12].Previous post-mortem[13] and neuroimaging [14,15] studies have shown that the medial temporal lobe (MTL) (Braak stages I-III) is the site of cortical neuro brillary tau tangle progression in the brain, followed by the temporal neocortex (Braak stages III-IV), and nally the parietal, occipital, and frontal lobes (Braak stages V) [16,17]. The initial tau aggregations can be seen in the entorhinal region of Aβ-negative people [11,18], but they seldom spread outside of the neocortex without a signi cant amount of neocortical Aβ plaques [11,[19][20][21][22]. Tau tangles [11,[23][24][25] and baseline Aβ plaques [8][9][10][11] can both indicate a faster tau accumulation in AD.…”

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confidence: 99%

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Initial levels of β-amyloid and tau deposition have distinct effects on longitudinal tau accumulation in Alzheimer’s disease

Cai

et al. 2022

Preprint

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Background Clarifying the relative roles of β-amyloid (Aβ) plaques and tau tangles in longitudinal tau replication and propagation inside and outside the medial temporal lobe (MTL) as well as how age, sex, APOE-4, and Klotho-VS heterozygosity (KL-VShet) modulate their relationships in Alzheimer’s disease (AD) may help with designing therapeutic clinical trials for AD and aid in our understanding of the disease’s symptoms. Methods We divided the 325 Aβ PET positive (A+) participants in this study into two groups, A+/T- (143) and A+/T+ (182), based on the threshold (1.25) of the Temporal-metaROI 18F-flortaucipir (FTP) standardized uptake value ratio (SUVR). We compared the baseline and slopes of A+/T- and A+/T + individuals’ Aβ plaques and Temporal-metaROI tau tangles with those of 162 A-/T- cognitively normal individuals without neurodegeneration. We also looked into how baseline Aβ and tau predict longitudinal tau increases and how age, sex, APOE-4, and KL-VShet affect these associations. Results In entorhinal, amygdala, and parahippocampal (early tau-affected regions of Temporal-metaROI), we found baseline Aβ and tau deposition were positively linked with more rapid tau increases in A+/T- participants. However, in A+/T + individuals, the longitudinal tau propagation of the fusiform, inferior temporal, and middle temporal cortices (late tau-affected regions of Temporal-metaROI) was primarily fueled by the presence of tau tangles rather than Aβ plaques. Furthermore, compared to older people (age≥65), younger individuals (age≤65) had faster Aβ-dependent but slower tau-related tau accumulation. Additionally, compared to the KL-VShet− group, KL-VShet+ carriers showed significantly less longitudinal tau accumulation associated with baseline entorhinal tau in the fusiform and inferior temporal regions. Conclusion These findings offer novel perspectives on developing AD treatment plans and aid in understanding tau replication and propagation inside and outside MTL in AD. In particular, decreasing Aβ plaques might be adequate for A+/T- persons but may not be sufficient for A+/T + individuals in preventing tau propagation and subsequent downstream effects connected to tau.

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

Cited by 110 publications

References 59 publications

Intrinsic connectivity of the human brain provides scaffold for tau aggregation in clinical variants of Alzheimer’s disease

Intrinsic connectivity of the human brain provides scaffold for tau aggregation in clinical variants of Alzheimer’s disease

Multi-policy models of interregional communication in the human connectome

Initial levels of β-amyloid and tau deposition have distinct effects on longitudinal tau accumulation in Alzheimer’s disease

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