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Young CB, Winer JR, Younes K, Cody KA, Betthauser TJ, Johnson SC, Schultz A, Sperling RA, Greicius MD, Cobos I, Poston KL, Mormino EC, Alzheimer’s Disease Neuroimaging Initiative and the Harvard Aging Brain Study. Divergent Cortical Tau Positron Emission Tomography Patterns Among Patients With Preclinical Alzheimer Disease. JAMA Neurol. 2022 Jun 1;79(6):592-603. PubMed.
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University of Pennsylvania
VU University Medical Center
Ludwig Maximilians University
Lund University
This timely and well-executed study by Christina Young and colleagues focuses on divergence from the common regional expression of Alzheimer’s disease tau pathology during the preclinical disease stages.
Much about the AD pathological cascade appears to follow a fairly stereotyped progression, but there are many exceptions. The typical progression of AD tau has been famously codified into the Braak staging system (Braak et al., 2006), but seminal autopsy work has drawn attention to the fact that many individuals express regional tau patterns that do not fit neatly into this staging regime (Murray et al., 2011).
Meanwhile, AD patients presenting at the clinic with primary or predominating non-amnestic syndromes that defy the typical AD presentation are not uncommon, particularly in early onset cases where about a third of patients have non-amnestic AD. Tau-PET studies have begun to bridge these findings, showing that atypical, e.g., visual- or language-predominant clinical presentations of AD, often show unique regional patterns of tau pathology (Ossenkoppele et al., 2016).
Recent large tau-PET studies by our group have further supported the notion that individual differences abound in the expression of tau pathology (Franzmeier et al., 2020), and that these differences may manifest as systematic AD tau subtypes (Vogel et al., 2021). Studying this variation may be important for individualized treatment and may reveal biological insights about AD pathogenesis, but it is also important for the development of generalizable biomarkers that capture AD tau pathology across all of its regional manifestations.
Like some of the aforementioned studies, Young and co-authors explore individual heterogeneity in tau-PET patterns within an aggregated multi-cohort sample that features very little overlap with previous studies. However, the present study uniquely focuses exclusively on clinically unimpaired old adults, resembling the population likely to be recruited in the next generation of AD drug trials that aim to intervene before symptom onset. That heterogeneous tau patterns emerge in this asymptomatic population is novel and interesting, and demonstrates that deviations from the stereotypical Braak staging system already occur during the earliest stages of tau accumulation. This finding does have important implications for aforementioned clinical trials, as individual variation in tau patterning may be indicative of similar variation in treatment response or disease progression (potentially in a cognitive-domain specific manner).
This excellent study concludes that the frequency of atypical tau patterns is around 9 percent, which the authors say coincides well with one estimate of prevalence of atypical late-onset AD (6 percent). It is worth noting that this estimate was among all amyloid-positive individuals—the prevalence of atypical tau patterns was 23.6 percent among amyloid- and tau-positive individuals.
It is important to acknowledge that these estimates can depend highly on the definition of the entities in question. For example, the present study quantifies “abnormal” as 3 standard deviations from an a priori control group, whereas our previous work used a threshold of 2 SDs from a data-driven control group and, unsurprisingly, found a higher proportion of individuals with deviant tau patterns. Similarly, there is a great deal of heterogeneity in cognitive deficits among MCI and AD patients (Scheltens et al., 2017; Groot et al., 2021) that may be considered “atypical” depending on the definition. However, the authors make an excellent point that our field’s collective focus on amnestic presentations, particularly in MCI, may bias our estimates, and even our definitions of what is “typical.”
One other interesting finding among a great many in this study was that MRI indices of neurodegeneration were not consistently informative toward tau patterns. Hippocampal volume was reduced in both “typical” and “atypical” tau groups, and reductions in cortical thickness did not match regions of tau accumulation. This may be yet another source of individual variation (Das et al., 2021), perhaps due to premorbid differences in brain structure and the influence of co-pathologies. These findings suggest that MRI alone may not be sufficient to serve as a proxy of tau subtypes in asymptomatic individuals.
There is still a great deal to learn about heterogeneity in the progression of tau pathology, and what promise it may hold for improving our understanding of AD. Longitudinal studies will help this pursuit, as will better standardization of what is considered abnormal, and a continuation of trends toward collaboration across different research groups. This excellent study from Dr. Mormino’s group represents an important and well-presented step along this path!
References:
Braak H, Alafuzoff I, Arzberger T, Kretzschmar H, Del Tredici K. Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathol. 2006 Oct;112(4):389-404. PubMed.
Murray ME, Graff-Radford NR, Ross OA, Petersen RC, Duara R, Dickson DW. Neuropathologically defined subtypes of Alzheimer's disease with distinct clinical characteristics: a retrospective study. Lancet Neurol. 2011 Sep;10(9):785-96. PubMed.
Ossenkoppele R, Schonhaut DR, Schöll M, Lockhart SN, Ayakta N, Baker SL, O'Neil JP, Janabi M, Lazaris A, Cantwell A, Vogel J, Santos M, Miller ZA, Bettcher BM, Vossel KA, Kramer JH, Gorno-Tempini ML, Miller BL, Jagust WJ, Rabinovici GD. 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.
Franzmeier N, Dewenter A, Frontzkowski L, Dichgans M, Rubinski A, Neitzel J, Smith R, Strandberg O, Ossenkoppele R, Buerger K, Duering M, Hansson O, Ewers M. Patient-centered connectivity-based prediction of tau pathology spread in Alzheimer's disease. Sci Adv. 2020 Nov;6(48) Print 2020 Nov PubMed.
Vogel JW, Young AL, Oxtoby NP, Smith R, Ossenkoppele R, Strandberg OT, La Joie R, Aksman LM, Grothe MJ, Iturria-Medina Y, Alzheimer’s Disease Neuroimaging Initiative, Pontecorvo MJ, Devous MD, Rabinovici GD, Alexander DC, Lyoo CH, Evans AC, Hansson O. Four distinct trajectories of tau deposition identified in Alzheimer's disease. Nat Med. 2021 May;27(5):871-881. Epub 2021 Apr 29 PubMed.
Scheltens NM, Tijms BM, Koene T, Barkhof F, Teunissen CE, Wolfsgruber S, Wagner M, Kornhuber J, Peters O, Cohn-Sheehy BI, Rabinovici GD, Miller BL, Kramer JH, Scheltens P, van der Flier WM, Alzheimer's Disease Neuroimaging Initiative, German Dementia Competence Network, University of California San Francisco Memory and Aging Center, Amsterdam Dementia Cohort. Cognitive subtypes of probable Alzheimer's disease robustly identified in four cohorts. Alzheimers Dement. 2017 Apr 17; PubMed.
Groot C, Grothe MJ, Mukherjee S, Jelistratova I, Jansen I, van Loenhoud AC, Risacher SL, Saykin AJ, Mac Donald CL, Mez J, Trittschuh EH, Gryglewski G, Lanzenberger R, Pijnenburg YA, Barkhof F, Scheltens P, van der Flier WM, Crane PK, Ossenkoppele R. Differential patterns of gray matter volumes and associated gene expression profiles in cognitively-defined Alzheimer's disease subgroups. Neuroimage Clin. 2021;30:102660. Epub 2021 Apr 3 PubMed.
Das SR, Lyu X, Duong MT, Xie L, McCollum L, de Flores R, DiCalogero M, Irwin DJ, Dickerson BC, Nasrallah IM, Yushkevich PA, Wolk DA, Alzheimer's Disease Neuroimaging Initiative. Tau-Atrophy Variability Reveals Phenotypic Heterogeneity in Alzheimer's Disease. Ann Neurol. 2021 Nov;90(5):751-762. Epub 2021 Oct 15 PubMed.
Stanford University School of Medicine
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.
References:
Thal DR, Rüb U, Orantes M, Braak H. Phases of A beta-deposition in the human brain and its relevance for the development of AD. Neurology. 2002 Jun 25;58(12):1791-800. PubMed.
Braak H, Alafuzoff I, Arzberger T, Kretzschmar H, Del Tredici K. Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathol. 2006 Oct;112(4):389-404. PubMed.
Vogel JW, Young AL, Oxtoby NP, Smith R, Ossenkoppele R, Strandberg OT, La Joie R, Aksman LM, Grothe MJ, Iturria-Medina Y, Alzheimer’s Disease Neuroimaging Initiative, Pontecorvo MJ, Devous MD, Rabinovici GD, Alexander DC, Lyoo CH, Evans AC, Hansson O. Four distinct trajectories of tau deposition identified in Alzheimer's disease. Nat Med. 2021 May;27(5):871-881. Epub 2021 Apr 29 PubMed.
Ossenkoppele R, Schonhaut DR, Schöll M, Lockhart SN, Ayakta N, Baker SL, O'Neil JP, Janabi M, Lazaris A, Cantwell A, Vogel J, Santos M, Miller ZA, Bettcher BM, Vossel KA, Kramer JH, Gorno-Tempini ML, Miller BL, Jagust WJ, Rabinovici GD. 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 YoungMayo Clinic
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., 2021; Wiepert 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.
References:
Graff-Radford J, Yong KX, Apostolova LG, Bouwman FH, Carrillo M, Dickerson BC, Rabinovici GD, Schott JM, Jones DT, Murray ME. New insights into atypical Alzheimer's disease in the era of biomarkers. Lancet Neurol. 2021 Mar;20(3):222-234. PubMed.
Jones DT, Graff-Radford J, Lowe VJ, Wiste HJ, Gunter JL, Senjem ML, Botha H, Kantarci K, Boeve BF, Knopman DS, Petersen RC, Jack CR Jr. Tau, amyloid, and cascading network failure across the Alzheimer's disease spectrum. Cortex. 2017 Dec;97:143-159. Epub 2017 Oct 3 PubMed.
Jones DT, Knopman DS, Gunter JL, Graff-Radford J, Vemuri P, Boeve BF, Petersen RC, Weiner MW, Jack CR Jr, Alzheimer’s Disease Neuroimaging Initiative. Cascading network failure across the Alzheimer's disease spectrum. Brain. 2016 Feb;139(Pt 2):547-62. Epub 2015 Nov 19 PubMed.
Sintini I, Graff-Radford J, Jones DT, Botha H, Martin PR, Machulda MM, Schwarz CG, Senjem ML, Gunter JL, Jack CR, Lowe VJ, Josephs KA, Whitwell JL. Tau and Amyloid Relationships with Resting-state Functional Connectivity in Atypical Alzheimer's Disease. Cereb Cortex. 2021 Feb 5;31(3):1693-1706. PubMed.
Wiepert DA, Lowe VJ, Knopman DS, Boeve BF, Graff-Radford J, Petersen RC, Jack CR Jr, Jones DT. A robust biomarker of large-scale network failure in Alzheimer's disease. Alzheimers Dement (Amst). 2017;6:152-161. Epub 2017 Jan 25 PubMed.
Jones D, Lowe V, Graff-Radford J, Botha H, Barnard L, Wiepert D, Murphy MC, Murray M, Senjem M, Gunter J, Wiste H, Boeve B, Knopman D, Petersen R, Jack C. A computational model of neurodegeneration in Alzheimer's disease. Nat Commun. 2022 Mar 28;13(1):1643. PubMed.
Therneau TM, Knopman DS, Lowe VJ, Botha H, Graff-Radford J, Jones DT, Vemuri P, Mielke MM, Schwarz CG, Senjem ML, Gunter JL, Petersen RC, Jack CR Jr. Relationships between β-amyloid and tau in an elderly population: An accelerated failure time model. Neuroimage. 2021 Nov 15;242:118440. Epub 2021 Jul 29 PubMed.
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